The DOI matches its Crossref record; 140 of the 168 references it cites were found in a registry or an index; 10 of the 54 sources its claims cite were checked for support.
Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
Open accessAbstract
With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI.
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Claims the paper makes
28 No full text of the sources · 21 No basis given · 18 Own interpretation · 9 Sources not found · 5 Couldn’t confirm · 3 Supported · 1 Supported so far · 1 Partly supported · 1 Not checked yet · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Stamoulis et al. [106] cast the adaptive DNN model selection issue as a hyperparameter optimization problem by taking into account the accuracy and communication constraints imposed by the devices.Then, a Bayesian optimization (BO) is adopted to solve this problem, achieving an improvement by up to 6× in terms of minimum energy per image under accuracy constraints.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > Park et al.
What the sources say
- Supportedcites [106] 106. D. Stamoulis et al., "Designing adaptive neural networks for energy-constrained image classification," in Proc. ACM ICCAD, 2018, Art. no. 23. source
Section 4.1 (p. 4) formulates adaptive CNN design, including network selection, as a hyperparameter optimization problem. Section 4.2 (p. 4) says the authors adapt Bayesian optimization to solve it, and the Conclusion (p. 8) reports up to 6× lower minimum energy per image under accuracy constraints than previously published methods. (LLM, conf=0.94)
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- Supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“The result varies with architectures and transfer techniques.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
What the sources say
- Not checked — no full text of the sourcecites [62] 62. R. Sharma, S. Biookaghazadeh, B. Li, and M. Zhao, "Are existing knowledge transfer techniques effective for deep learning with edge devices?" in Proc. IEEE Int. Conf. Edge Comput. (EDGE), Jul. 2018, pp. 42-49. source
- Could not verifycites [44] 44. Q. Chen, Z. Zheng, C. Hu, D. Wang, and F. Liu, "Data-driven task allocation for multi-task transfer learning on the edge," in Proc. IEEE 39th Int. Conf. Distrib. Comput. Syst. (ICDCS), 2019. sourceExperimental — accuracy not yet measured
Section 5.2.2 (p. 6) reports accuracy for one architecture under different distributed-training conditions, but does not compare architectures or transfer-learning techniques. Section 6 (p. 6) calls studies of other DNN architectures future work. Searches for transfer learning and variation across architectures or transfer techniques found no passage addressing the claim. (LLM, conf=0.76)
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The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Cachier can increase responsiveness by 3× or more.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) Edge Caching:
What the sources say
- Could not verifycites [93] 93. U. Drolia, K. Guo, J. Tan, R. Gandhi, and P. Narasimhan, "Cachier: Edge-caching for recognition applications," in Proc. IEEE ICDCS, Jun. 2017, pp. 276-286. sourceExperimental — accuracy not yet measured
The Results section discusses Edgent’s inference latency, but does not report a result for Cachier. Searches for “Cachier” and “responsiveness three times” found no passage supporting a 3× improvement. (LLM, conf=0.77)
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Data Generated at the Network Edge Need AI to Fully Unlock Their Potential”
- Also stated in: § A. Motivation and Benefits of Edge Intelligence · boosted
“AI will be functionally necessary due to its ability to quickly analyze those huge data volumes and extract insights from them for high-quality decision making.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > A. Motivation and Benefits of Edge Intelligence
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- Also stated in: § A. Motivation and Benefits of Edge Intelligence · boosted
In the paper
“Cisco estimates that nearly 850 ZB will be generated by all people, machines, and things at the network edge by 2021 [3].”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > I. I N T R O D U C T I O N
What the sources say
- Not matchedcites [3] 3. Cisco Global Cloud Index: Forecast and Methodology, 2016-2021, White Paper. [Online]. Available: https://www.cisco.com/c/en/us/ solutions/collateral/service-provider/global- cloud-index-gci/white-paper-c11-738085.html
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedged- Asserted: passes — The authors put it forward as true.
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In the paper
“Lin et al. [53] observe that 99.9% of the gradient exchange in distributed SGD (DSGD) are redundant, which demonstrates the power of gradient compression.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 3) Gradient Compression:
What the sources say
- Not checked — no full text of the sourcecites [53] 53. Y. Lin, S. Han, H. Mao, Y. Wang, and W. J. Dally, "Deep gradient compression: Reducing the communication bandwidth for distributed training," 2017, arXiv:1712.01887. [Online]. Available: https://arxiv.org/abs/1712.01887 source
boosted- Asserted: passes — The authors put it forward as true.
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In the paper
“Specifically, while preserving client's own privacy, the training loss can be reduced by sharing the models among clients, comparing to training solely on their own inputs.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > C. Enabling Technologies
What the sources say
- Not checked — no full text of the sourcecites [46] 46. R. Shokri and V. Shmatikov, "Privacy-preserving deep learning," in Proc. 22nd ACM SIGSAC Conf. Comput. Commun. Secur., 2015, pp. 1310-1321. source
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Edge Computing Is Able to Prosper AI With Richer Data and Application Scenarios”
- Also stated in: § 2) Edge Computing Is Able to Prosper AI With Richer Data and Application Scenarios: · hedged
“edge computing is proposed to achieve low-latency data processing by sinking the computing capability from the cloud data center to the edge side, i.e., data generation source, which may enable AI processing with high performance.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 2) Edge Computing Is Able to Prosper AI With Richer Data and Application Scenarios:
plaindirect- Asserted: passes — The authors put it forward as true.
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- Also stated in: § 2) Edge Computing Is Able to Prosper AI With Richer Data and Application Scenarios: · hedged
In the paper
“A flaw of [46] is that it does not consider unbalanced and nonindependent identical distribution (non-IID) data.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > C. Enabling Technologies
What the sources say
- Not checked — no full text of the sourcecites [46] 46. R. Shokri and V. Shmatikov, "Privacy-preserving deep learning," in Proc. 22nd ACM SIGSAC Conf. Comput. Commun. Secur., 2015, pp. 1310-1321. source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“big data have recently gone through a radical shift of data source from the megascale cloud datacenters to the increasingly widespread end devices, e.g., mobile devices and Internetof-Things (IoT) devices.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > I. I N T R O D U C T I O N
What the sources say
- Not matchedcites [3] 3. Cisco Global Cloud Index: Forecast and Methodology, 2016-2021, White Paper. [Online]. Available: https://www.cisco.com/c/en/us/ solutions/collateral/service-provider/global- cloud-index-gci/white-paper-c11-738085.html
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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- Probably not a substantial claim. 28
- It may state two separate claims as one. 27
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“VGG-16 [19] is trained to a deeper structure of 16 layers consisting of 13 convolution layers and three fully connected layers, requiring 138 million weights and 15.5G MACs to classify the image with a size of 224 × 224.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 1) Convolution Neural Network:
What the sources say
- Partly supportedcites [19] 19. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," 2014, arXiv:1409.1556. [Online]. Available: https://arxiv.org/abs/1409.1556 sourceExperimental — accuracy not yet measured
Table 1 (p. 3) identifies configuration D as a 16-weight-layer network with a 224 × 224 RGB input, and Table 2 (p. 3) lists 138 million parameters for D. Section 2.1 (p. 2) describes the three fully connected layers. Our copy does not report the claimed 15.5G MACs per image, so that part is unsupported. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“In particular, an update aggregation protocol named FedCS is developed to allow the centralized server to aggregate as many client updates as possible and to accelerate performance improvement in ML models.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 2) Aggregation Frequency Control:
What the sources say
- Could not verifycites [52] 52. T. Nishio and R. Yonetani, "Client selection for federated learning with heterogeneous resources in mobile edge," 2018, arXiv:1804.08333. [Online]. Available: https://arxiv.org/abs/ 1804.08333 sourceExperimental — accuracy not yet measured
The Introduction (p. 2) describes pruning, quantization, and Huffman coding, while §6.3 (pp. 9–10) discusses inference speedups from compression. Searches for FedCS, client-update aggregation, and federated client selection found no passage addressing the claimed protocol or its effect on model improvement. (LLM, conf=0.76)
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Could not verify
Every source the paper cites for this claim was checked and supports it.
In the paper
“The optimization in [45] emphasizes the properties of unbalanced and non-IID since the distributed data may come from various sources.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > C. Enabling Technologies
What the sources say
- Supportedcites [45] 45. H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Y. Arcas, "Communication-efficient learning of deep networks from decentralized data," 2016, arXiv:1602.05629. [Online]. Available: https://arxiv.org/abs/1602.05629 source
The Introduction (p. 2) explicitly says the work emphasizes the non-IID and unbalanced properties of federated optimization. It explains that clients’ data reflect different users’ device usage and that users contribute varying amounts of data, supporting the citing sentence’s explanation of why those properties arise. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“when moving a tremendous amount of data across the wide area network (WAN), both monetary cost and transmission delay can be prohibitively high, and the privacy leakage can also be a major concern [5].”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > I. I N T R O D U C T I O N
What the sources say
- Could not verifycites [5] 5. Q. Pu et al., "Low latency geo-distributed data analytics," in Proc. ACM SIGCOMM, 2015, pp. 421-434. sourceExperimental — accuracy not yet measured
The Introduction and Section 6 (Advantages and disadvantages; page numbers unavailable) discuss generative models and their computational properties, not transferring large datasets over a WAN. Searches for WAN costs, transmission delay, and privacy leakage found no passage addressing the claim. (LLM, conf=0.75)
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Could not verify
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“the physical proximity between the computing and information-generation sources promises several benefits compared to the traditional cloud-based computing paradigm, including low latency, energy efficiency, privacy protection, reduced bandwidth consumption, on-premises, and context awareness [6], [8].”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > I. I N T R O D U C T I O N
What the sources say
- Not checked — no full text of the sourcecites [6] 6. W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge computing: Vision and challenges," IEEE Internet Things J., vol. 3, no. 5, pp. 637-646, Oct. 2016. source
- Supportedcites [8] 8. Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, "A survey on mobile edge computing: The communication perspective," IEEE Commun. Surveys Tuts., vol. 19, no. 4, pp. 2322-2358, 4th Quart., 2017. source
The comparison in “Mobile Edge Computing Versus Mobile Cloud Computing” (p. 3) identifies lower latency, mobile-device energy savings, context awareness, and improved privacy and security as MEC advantages. “Mobile Computing for 5G: From Clouds to Edges” (p. 1) explains that moving traffic toward the edge avoids burdening backhaul networks, while the privacy discussion (p. 3) gives enterprise-managed deployment as an example of keeping data on premises. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“A good performance improvement is obtained by transferring knowledge from both the intermediate layers and last layer of the teacher to a shallower student while other architectures and transfer techniques do not fare so well and some of them even lead to negative performance impact.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
What the sources say
- Not checked — no full text of the sourcecites [62] 62. R. Sharma, S. Biookaghazadeh, B. Li, and M. Zhao, "Are existing knowledge transfer techniques effective for deep learning with edge devices?" in Proc. IEEE Int. Conf. Edge Comput. (EDGE), Jul. 2018, pp. 42-49. source
- Could not verifycites [44] 44. Q. Chen, Z. Zheng, C. Hu, D. Wang, and F. Liu, "Data-driven task allocation for multi-task transfer learning on the edge," in Proc. IEEE 39th Int. Conf. Distrib. Comput. Syst. (ICDCS), 2019. sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
Every source the paper cites for this claim was checked and supports it.
In the paper
“The early work on random gossip algorithms is gossip averaging [63], which can fast converge toward a consensus among nodes by exchanging information peer to peer.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
What the sources say
- Supportedcites [63] 63. S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, "Randomized gossip algorithms," IEEE Trans. Inf. Theory, vol. 52, no. 6, pp. 2508-2530, Jun. 2006. source
The paper describes gossip averaging as nodes communicating with randomly chosen neighbors (Randomized Gossip Algorithms, p. 1). It studies convergence to the vector of averages and the design of the fastest gossip algorithm (Previous Results, p. 3; Randomized Gossip Algorithms, p. 1), supporting the peer-to-peer consensus paraphrase. (LLM, conf=0.89)
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“The algorithm is based on the convergence analysis of distributed gradient descent and can be applied to federated learning in edge computing with provable convergence.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 2) Aggregation Frequency Control:
What the sources say
- Not matchedcites [51] 51. S. Wang et al., "Adaptive federated learning in resource constrained edge computing systems," IEEE J. Sel. Areas Commun., vol. 37, no. 3, pp. 1205-1221, Jun. 2019.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a result, the transmission latency of data offloading decreases, the data privacy increases, and the WAN bandwidth cost reduces.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 5) Level 4-Cloud-Edge Cotraining and Inference: Train-
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The main challenges of federated learning are optimization and communication.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > C. Enabling Technologies
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While edge computing and AI complement each other from a technical perspective, their application and popularization are also mutually beneficial.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 2) Edge Computing Is Able to Prosper AI With Richer Data and Application Scenarios:
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Though the federated learning technique exploits a new decentralized deep learning architecture, it is built upon a central server for aggregating local updates.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > C. Enabling Technologies
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“As forecast by Gartner [33], more than 80% of enterprise IoT projects will include an AI component by 2022, up from only 10% today.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > A. Motivation and Benefits of Edge Intelligence
What the sources say
- Not matchedcites [33] 33. AI Trends for Enterprise Computing. [Online]. Available: https://www.gartner.com/ smarterwithgartner/3-ai-trends-for-enterprise- computing/
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This conflict indicates that there is no”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 5) Level 4-Cloud-Edge Cotraining and Inference: Train-
hedged- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Energy efficiency is mainly affected by the size of the target training model and resources of the used devices.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 6) Energy Efficiency:
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“ResNet [20], the state-of-the-art effort, uses the”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 1) Convolution Neural Network:
What the sources say
- Not checked — no full text of the sourcecites [20] 20. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. IEEE CVPR, Jun. 2016, pp. 770-778. source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“real-time video analytics is envisioned to be a killer application for edge computing [12], [37], [38].”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) Edge Computing Can Be Popularized With AI Applications:
What the sources say
- Not checked — no full text of the sourcecites [12] 12. G. Ananthanarayanan et al., "Real-time video analytics: The killer app for edge computing," Computer, vol. 50, no. 10, pp. 58-67, 2017. source
- Not checked — no full text of the sourcecites [37] 37. H. Zhang, G. Ananthanarayanan, P. Bodik, M. Philipose, P. Bahl, and M. J. Freedman, "Live video analytics at scale with approximation and delay-tolerance," in Proc. USENIX NSDI, 2017, pp. 377-392.
- Not checked — no full text of the sourcecites [38] 38. C.-C. Hung et al., "VideoEdge: Processing camera streams using hierarchical clusters," in Proc. IEEE/ACM Symp. Edge Comput. (SEC), Oct. 2018, pp. 115-131. source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“AlexNet requires 61 million weights and 724 million MACs (multiply-add computation) to classify the image with a size of 227 × 227.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 1) Convolution Neural Network:
What the sources say
- Not matchedcites [18] 18. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Proc. NIPS, 2012, pp. 1097-1105.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“However, Gaia focuses on geodistributed datacenters that are capacity-unlimited, making it is not generally applicable to edge computing nodes whose capacity is highly constrained.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 2) Aggregation Frequency Control:
What the sources say
- Not matchedcites [50] 50. K. Hsieh, A. Harlap, N. Vijaykumar, D. Konomis, G. R. Ganger, and P. B. Gibbons, "Gaia: Geo-distributed machine learning approaching LAN speeds," in Proc. NSDI, 2017, pp. 629-647.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Under the decentralized training mode, the convergence value depends on the way the gradient is synchronized and updated.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 2) Convergence:
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“When training the DNN model by using the data originated at a massive of end devices, the raw data or intermediate data should be transferred out of the end devices.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 3) Privacy:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Deep learning represents the state-of-the-art AI technology as well as a highly resource-demanding workload that naturally suits for edge computing.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > B. Deep Learning and Deep Neural Networks
plaindirect- Asserted: passes — The authors put it forward as true.
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- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“When training the DNN model in a decentralized manner, both the computation and communication processes consume enormous energy.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 6) Energy Efficiency:
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: For most end devices, they are energy-constrained.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, this is achieved at the cost of increased computational latency and energy consumption.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 5) Level 4-Cloud-Edge Cotraining and Inference: Train-
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“GoogleNet achieves a better accuracy performance than VGG-16, while only requiring seven million weights and 1.43G MACs to process the image with the same size.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 1) Convolution Neural Network:
What the sources say
- Not checked — no full text of the sourcecites [28] 28. C. Szegedy et al., "Going deeper with convolutions," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 1-9. source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“MeDNN improves the DNN model inference by 1.86-2.44×with two to four worker nodes and saves 26.5% of additional computing time and 14.2% of extra communication time.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 1) Model Compression:
What the sources say
- Not checked — no full text of the sourcecites [84] 84. J. Mao et al., "MeDNN: A distributed mobile system with enhanced partition and deployment for large-scale DNNs," in Proc. 36th Int. Conf. Comput.-Aided Design, Nov. 2017, pp. 751-756. source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Communication overhead is affected by the size of the original input data, the way of transmission, and the available bandwidth.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > VII. C O N C L U S I O N
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Due to the combinatorial nature, the high-dimensional configuration problem involves a huge search space of parameters and is very challenging to tackle.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > D. Summary of Existing Systems and Frameworks
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Standard performance indicators such as top-k accuracy or mean average precision fail to reflect the runtime performance of DNN model inference on edge devices.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, data parallelism may bring heavy overhead of communication, while model parallelism usually leads to severe underutilization of computation resources.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
In the paper
“PipeDream shows the advantage of reducing communication overhead and utilizing computing resource efficiently.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
What the sources say
- Not checked — no full text of the sourcecites [61] 61. A. Harlap, D. Narayanan, A. Phanishayee, V. Seshadri, G. R. Ganger, and P. B. Gibbons, "PipeDream: Fast and efficient pipeline parallel DNN training," 2018, arXiv:1806.03377. [Online]. Available: https://arxiv.org/abs/1806.03377 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The communication latency may vary from the size of transmitted raw or intermediate data and the bandwidth of network connection.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > VII. C O N C L U S I O N
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“By deployment on large-scale systems, Daily et al. [66] show that the trivial gossip-based algorithms at scale lead to a communication imbalance, poor convergence, and heavy communication overhead.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
What the sources say
- Not checked — no full text of the sourcecites [66] 66. J. Daily, A. Vishnu, C. Siegel, T. Warfel, and V. Amatya, "GossipGraD: Scalable deep learning using gossip communication based asynchronous gradient descent," 2018, arXiv:1803.05880. [Online]. Available: https://arxiv.org/ abs/1803.05880 source
boosteddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“FoggyCache reduces computation latency and energy consumption by a factor of 3× to 10×.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) Edge Caching:
What the sources say
- Not checked — no full text of the sourcecites [95] 95. P. Guo, B. Hu, R. Li, and W. Hu, "FoggyCache: Cross-device approximate computation reuse," in Proc. ACM Mobicom, 2018, pp. 19-34. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Arguably, latency is one of the most fundamental performance indicators of distributed DNN model training since it directly influences when the trained model is available for use.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > VII. C O N C L U S I O N
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This is based on the important observation that a DNN model can be split inside between two successive layers with two partitions deployed on different locations without losing accuracy.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“the proposed automatic optimization framework AdaDeep [75] systematically formulates the goals and constraints on accuracy, latency, storage, and energy into a unified optimization problem and leverages DRL to effectively find a good combination of compression techniques.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 1) Model Compression:
What the sources say
- Not checked — no full text of the sourcecites [75] 75. S. Liu, Y. Lin, Z. Zhou, K. Nan, H. Liu, and J. Du, "On-demand deep model compression for mobile devices: A usage-driven model selection framework," in Proc. 16th Annu. Int. Conf. Mobile Syst., Appl., Services, 2018, pp. 389-400. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The decentralized architecture is naturally friendly to user's privacy, for which the systems that are based on the decentralized architecture such as BlockFL and GossipGraD typically preserve privacy better.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > Table 2 Overview of Systems and Frameworks on EI Model Training
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Intuitively, this communication overhead increases the training latency, energy, and bandwidth consumption.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > VII. C O N C L U S I O N
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Precog uses predictions of Markov chains to prefetch data onto the mobile device and reach a speedup of 5×.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) Edge Caching:
What the sources say
- Not checked — no full text of the sourcecites [94] 94. U. Drolia, K. Guo, and P . Narasimhan, "Precog: Prefetching for image recognition applications at the edge," in Proc. ACM/IEEE Symp. Edge Comput. (SEC), Oct. 2017, p. 17. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Compared with the DNN training under the cloud-based framework, the DNN training under edge-based framework pays more attention to protecting users' privacy and training an available deep learning model faster.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > Table 2 Overview of Systems and Frameworks on EI Model Training
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The latency of the distributed training process typically consists of the computation latency and the communication latency.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > VII. C O N C L U S I O N
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Both Blot et al. [64] and Jin et al. [65] apply gossip algorithms on the updates of SGD, but neither of them has performance convergence degradation at large scale.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
What the sources say
- Not checked — no full text of the sourcecites [64] 64. M. Blot, D. Picard, M. Cord, and N. Thome, "Gossip training for deep learning," 2016, arXiv:1611.09726. [Online]. Available: https://arxiv.org/abs/1611.09726 source
- Not checked — no full text of the sourcecites [65] 65. P. H. Jin, Q. Yuan, F. Iandola, and K. Keutzer, "How to scale distributed deep learning?" 2016, arXiv:1611.04581. [Online]. Available: https://arxiv.org/abs/1611.04581 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“ReXCam reduces computation workload by 4.6× and improves DNN model inference accuracy by 27%.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 5) Input Filtering:
What the sources say
- Not checked — no full text of the sourcecites [99] 99. S. Jain, J. Jiang, Y. Shu, G. Ananthanarayanan, and J. Gonzalez, "ReXCam: Resource-efficient, cross-camera video analytics at enterprise scale," 2018, arXiv:1811.01268. [Online]. Available: https://arxiv.org/abs/1811.01268 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“For the object recognition application, Huang et al. [120] investigate the influence of the main factors, e.g., number of proposals, input image size, and the selection of feature extractor, on inference speed and accuracy.Based on their experiment results, a new combination of these factors is found to outperform the state-of-the-art method.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > D. Tradeoff Design With Various DNN Performance Metrics
What the sources say
- Not checked — no full text of the sourcecites [120] 120. J. Huang et al., "Speed/accuracy trade-offs for modern convolutional object detectors," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 7310-7311. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Standard performance indicators such as top-k accuracy or mean average precision fail to reflect the runtime performance of DNN model inference on edge devices.
In the paper
“Compared to the approach that uploads the entire model, IONN significantly improves query performance and energy consumption during DNN model uploading.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 1) Model Compression:
What the sources say
- Not checked — no full text of the sourcecites [82] 82. H.-J. Jeong, H.-J. Lee, C. H. Shin, and S.-M. Moon, "IONN: Incremental offloading of neural network computations from mobile devices to edge servers," in Proc. ACM Symp. Cloud Comput., 2018, pp. 401-411. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The gossip distributed algorithms enjoy the advantage of full asynchronization and total decentralization as they have no requirement on centralized nodes or variables.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“the cascading network [91] simply adds the MP layer and fully connected layer to the standard DNN model and achieves a speedup of 20%.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > The aggregation methods work when multiple mobile
What the sources say
- Not checked — no full text of the sourcecites [91] 91. S. Leroux et al., "The cascading neural network: Building the Internet of smart things," Knowl. Inf. Syst., vol. 52, no. 3, pp. 791-814, 2017.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This process will tend to work if the features are general (i.e., suitable to both base and target tasks) instead of specific to the base task.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 4) DNN Splitting:
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper relies on this without support.
There is a lack of a dedicated venue for summarizing, discussing, and disseminating recent advances of edge intelligence in both industry and academia.
“There is, in general, a lack of venue dedicated for summarizing, discussing, and disseminating the recent advances of EI, in both industrial and academia.”
The paper relies on this without support.
Research and practice on edge intelligence are still in a very early stage.
“Notably, research and practice on this emerging interdiscipline-EI-are still in a very early stage.”
The paper relies on this without support.
The edge ecosystem will present many novel application scenarios for AI and fuel the continuous booming of AI.
“Clearly, via bringing the huge volumes of data to AI, the edge ecosystem will present many novel application scenarios for AI and fuel the continuous booming of AI.”
The paper relies on this without support.
For most end devices, they are energy-constrained.
“However, for most end devices, they are energy-constrained.”
The paper relies on this without support.
It is inevitable to deal with privacy issues when raw or intermediate data are transferred out of end devices.
“Obviously, it is inevitable to deal with privacy issues in this scenario.”
The paper relies on this without support.
For some real-time intelligent mobile applications, they usually have stringent deadline requirement such as 100-ms latency.
“For some real-time intelligent mobile applications (e.g., AR/VR mobile gaming and intelligent robots), they usually have stringent deadline requirement such as 100-ms latency.”
The paper relies on this without support.
For some mobile applications requiring high reliability, they demand ultrahigh accuracy on DNN model inference.
“For some mobile applications requiring a high level of reliability, such as self-driving car and face authentication, they demand the ultrahigh accuracy on the DNN model inference.”
The paper relies on this without support.
Most existing efforts use a single compression technique.
“While most existing efforts use a single compression technique, they may not suffice to meet the diverse requirements and constraints on accuracy, latency, storage, and energy imposed by some IoT devices.”
The paper relies on this without support.
Standard performance indicators such as top-k accuracy or mean average precision fail to reflect the runtime performance of DNN model inference on edge devices.
“However, it is difficult for software developers to choose an appropriate DNN model for the EI application because the standard performance indicators such as top-k accuracy or mean average precision fail to reflect the runtime performance of DNN model inference on edge devices.”
The paper relies on this without support.
Most current edge AI programming/software platforms mainly serve as relays for connecting to cloud data centers.
“Nevertheless, currently, most of these platforms mainly serve as relays for connecting to the powerful cloud data centers.”
The paper relies on this without support.
Most deep learning-based AI models are highly resource-intensive.
“Most of the deep learning-based AI models are highly resourceintensive, which means that powerful computing capability supported by abundant hardware resources (e.g., GPU, field-programmable gate array (FPGA), and TPU) is important to boost the performance of these AI models.”
The paper relies on this without support.
For an EI application with a specific mission, there is usually a series of DNN model candidates that are capable of finishing the task.
“For an EI application with a specific mission, there is usually a series of DNN model candidates that are capable of finishing the task.”
The paper relies on this without support.
The service environments for many EI application scenarios are of high dynamics and it is hard to accurately predict future events.
“Since for many EI application scenarios (e.g., smart cities), the service environments are of high dynamics and it is hard to accurately predict future events.”
The paper relies on this without support.
The high-efficiency operation of EI services may require close collaboration and integration across different service providers.
“The high-efficiency operation of EI services may require close collaboration and integration across different service providers, e.g., for implementing expanded resource sharing and smooth service handover.”
The paper relies on this without support.
A user can be a service consumer and, meanwhile, a data generator as well.
“Also, for EI service, a user can be a service consumer and, meanwhile, a data generator as well.”
The paper relies on this without support.
There is a stringent need to push the AI frontier from the cloud to the network edge.
“Driving by flourishing of both AI and IoT, there is a stringent need to pushing the AI frontier from the cloud to the network edge.”
The paper relies on this without support.
Edge computing is widely recognized as a promising solution to support computation-intensive AI applications in resource-constrained environments.
“To fulfill this trend, edge computing has been widely recognized as a promising solution to support computation-intensive AI applications in resource-constrained environments.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Additionally, UAVs generate lots of data at the network edge and directly sharing original data sets between many edge nodes has a high risk associated with it [8].”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [8]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Edge Intelligence (EI) Zhou et al. (2019) proposes a new paradigm combining AI and edge computing, with the goal of performing distributed computing of DL models.”
From Deepfakes: current and future trends (2024) · cites this paper as Zhou et al. 2019
The cited source says what the claim attributes to it.
In the citing paper
“This entails some advantages such as low latency, energy and bandwidth efficiency, privacy protection, and context awareness (Zhou et al. 2019).”
From Deepfakes: current and future trends (2024) · cites this paper as Zhou et al. 2019
The cited source says what the claim attributes to it.
In the citing paper
“In the case of training a deep learning model in an edge computing environment, where distributed models are trained locally first and then updates are aggregated centrally (e.g., federated learning), the control of updates aggregation frequency has a significant influence on the communication overhead.”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [8]
In the citing paper
“Edge AI provides a promising solution for connected intelligence by enabling data collection, processing, transmission, and consumption at the network edge [29], [30].”
From Edge artificial intelligence for 6g: vision, enabling technologies, and applications (2022) · cites this paper as [30]
In the citing paper
“All of these reviews only have a small fraction to discuss the edge DNN design which is the key element of edge intelligence systems.”
From Bringing ai to edge: from deep learning’s perspective (2022) · cites this paper as [13]
In the citing paper
“Smart city [113] is another important application scenario for deep-learning-enabled edge computing.”
From Deep learning for edge computing applications: A State-of-the-Art survey (2020) · cites this paper as [113]
In the citing paper
“In the current scenario, Blockchain provides a high degree of privacy for health data [49].”
From 6G Communication Technology: A Vision on Intelligent Healthcare (2021) · cites this paper as [49]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > B. Key Performance Indicators
“To describe the service quality of the EI model inference, we introduce the following five metrics.”
§ Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing This article conducts a comprehensive survey of the research efforts on edge intelligence. It provides an overview of the architectures, frameworks, and emerging key > 6) Memory Footprint:
“## 6) Memory Footprint:”
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Sorted by citations
Items 2,001–2,100 of 2,413
Efficient TEE-Based DNN Inference on Edge Devices: A PyTorch-Compatible Design
Yu, Y., Guo, Y., Liu, Y., et al. (2025) · IEEE International Conference on High Performance Computing and Communications (HPCC) · doi:10.1109/hpcc67675.2025.00067
“Its low-latency and privacy-preserving features have driven widespread deployment in various intelligent applications [1].”
Bhise, S., Kshirsagar, K. P., & Deshpande, V. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06694-7_51
Concurrent Graph Data Sharing and Resource Optimization Model in Edge Computing
Xiu, W., Johar, M. G. M., Alkawaz, M. H., et al. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5006-4_83
Federated Learning for System-Aware In-Device Task Scheduling in IoT Ecosystems
Tishin, M., Mavromoustakis, C. X., Batalla, J. M., et al. (2025) · IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) · doi:10.1109/camad67323.2025.11229893
“Originally developed by Google, FL enables a group of devices to collaboratively train a shared model without exchanging raw data [9], [10].”
HEMS:Heterogeneous Model Splitting Strategies Under Cross-Domain Computing Power Distribution
Li, H., Hao, H., Shi, H., et al. (2025) · doi:10.1109/ijcnn64981.2025.11228897
“With the rapid advancement of artificial intelligence technologies [1], methods such as deep reinforcement learning [2], which optimize model parameters by training multi-layer deep neural networks, have significantly contributed to intelligent services [3] like object detection, image recognition,…”
Kailani, O. A., Zhang, Y., & Zhao, W. (2025) · doi:10.1109/iccad66269.2025.11240881
“Artificial Intelligence (AI) has experienced significant growth in recent years, emerging as a transformative force that fundamentally alters various aspects of modern life, work, and technological interaction [1].”
Smart Intrusion Detection System for Restricted Maritime Zones
Babu, C. V. S., Thirumalai, E., Ragunath, S., & Sujin, S. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-3546-9.ch007
Wei, X., Wu, D., & Zhou, L. (2025) · Wireless networks · doi:10.1007/978-3-032-00957-9_4
Shinde, S. S., & Kiwelekar, A. W. (2025) · IEEE India Council International Subsections Conference (INDISCON) · doi:10.1109/indiscon66021.2025.11253889
“Networks must handle fast-moving data flows, and ML models need to process this data quickly to prevent delays [45].”
Cost-efficient Processing-in-Memory Architecture with Training-free and Universal Error Compensation
Yun, M., Min, J. G., Oh, S., Choi, J., Lee, J., Je, M., & Lee, Y. (2025) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1109/islped65674.2025.11261781
“Deep neural networks (DNNs) excel in various tasks [1]–[5], but in the Von-Neumann architecture, their reliance on external memory access (EMA) substantially increases energy consumption [6].”
Edge AI Consensus: Blockchain-Secured Federated Learning for Trustless Intelligence Network
Gupta, S., Vanteru, K., Gadiraju, P., et al. (2025) · doi:10.1145/3744504.3744519
“Low-latency inference, less bandwidth consumed, and privacy concerns are addressed, keeping the data local [1].”
Optimal Resource Management for Wireless Cooperative Edge Learning with Energy Harvesting
Binucci, F., & Banelli, P. (2025) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco63237.2025.11226517
“Edge Intelligence [1] has recently emerged as a crucial technology for delivering cost-effective, low-latency machine learning, and artificial intelligence services in next-generation mobile networks.”
Delay optimization of UAV assisted mobile edge computing in anti-jamming environment
Gao, J. B., Huan, Y., Chen, J., et al. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105821
Boukhobza, J., Burrello, A., Chang, Y.-H., Li, Y., Pagliari, D. J., Wu, C.-F., Yang, M.-C., & Yang, T.-Y. (2025) · International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES) · doi:10.1145/3742872.3758334
“An edge device can be of different sizes ranging from sensors, cameras, smartphones, IoT devices, and embedded systems, up to a micro data center with several server racks [78].”
Fan, L., Zeng, Z., Zhang, X., & Duan, M. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111938
Federated Curriculum Learning for Privacy-Preserving Personalization in Edge Environments
Mehta, A. D. (2025) · Darpan International Research Analysis · doi:10.36676/dira.v13.i4.182
Edge AI for Real-Time Decision Making in Resource-Constrained EnvironmentsPreprint
Primova, Z. (2025) · doi:10.24108/preprints-3114102
C-V2X Based Vehicle Movement Prediction for Connected Vehicle Application
Amin, M. H., Park, J., Konjeti, H. B., & Bhuduri, S. (2025) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme64568.2025.11277833
“Current autonomous vehicles (Levels 1-3) heavily rely on GPUs, which are energy-intensive [32, 34].”
Enabling Efficient Synergistic Multi-view Inference Across Heterogeneous Edge Devices
Dong, F., Chen, R., Fu, S., Cheng, W., Zhou, R., & Zhang, X. (2025) · ACM Transactions on Sensor Networks · doi:10.1145/3785358
Hsing, N.-W., Bo-Ya, P., & Chiang, P.-J. (2025) · Applied Optics · doi:10.1364/ao.581780
An Online Game-Integrated Auction Scheme for DDoS Mitigation in Mobile Edge Computing
Pan, J., Wu, J., & Zhang, Y. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3599552
Low Power Indirect Time of Flight Near Field LiDAR Depth Correction
Nagiub, M., Beuth, T., Sistu, G., et al. (2025) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2025.3613141
“The other side of the computer vision design equation shows that Edge AI computing and low-power systems built on ARM processors have become critical enablers for real-time, energy-efficient data processing in resource-constrained environments [10], [11].”
Connected Realities: Exploring IoT’s Technological Marvels and Varied Applications
Siraparapu, S. R., & Azad, S. M. A. K. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-76286-4_14 · Cited by 1
Distributed Training of Neural Networks in Smart Manufacturing Systems
Jayadharshini, P., Santhiya, S., Keerthika, S., Abinaya, N., Ahalya, R., & Nandhini, V. N. S. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-94637-0_9
Optimized Large AI Model for Real-Time ACEN Detection in Intelligent Mobile Edge Networks
Gaurav, A., Gupta, B. B., Zhou, L., et al. (2025) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps65812.2025.11152864
“Moreover, the combination of YOLO with MEC can use the edge network’s distributed computation and storage capacity to provide low-latency, efficient ACEN detection[9, 10].”
Lightweight Temporal Consistency for Grid-Based Obstacle Detection in Edge Devices
Kurkutlu, O., & Roohi, A. (2025) · IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) · doi:10.1109/acsos-c66519.2025.00047
“Cameras offer dense visual information without the need for specialized hardware like LiDAR or sonar [9].”
Resource Control in the Distributed Computer Systems Based on Neural Networks
Kulyk, V., Zavgorodnii, V., & Mukhin, O. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-97529-5_18
Poster: Towards AI-Native RAN: Architecture and Key Technologies
Wang, J., Li, Z., & Li, A. (2025) · doi:10.1145/3737900.3770169
“Driven by the proliferation of artificial i ntelligence (AI)- empowered applications, mobile networks are evolving to the sixth generation (6G) networks to deliver low-latency and ubiquitous services [1].”
Giacobbe, M., Carnevale, L., & Villari, M. (2025) · International Conference on the Internet of Things (IoT) · doi:10.1145/3770501.3770520
“According to [23], this work is designed at level three of the six-level rating for edge intelligence.”
Predictive Edge-Cloud Load Balancing Using Temporal Graph Neural Networks
Richter, D. T. (2025) · Universal Research Reports · doi:10.36676/urr.v12.i4.1652
Agent Shadowing: Edge-Driven Seamless Integration for Edge and Cloud AI Agents
Sarkar, M., Todkar, A., & Solanki, J. (2025) · doi:10.1145/3774900.3776637
“Implementing true bidirectional agent-to-agent channels on MQTT requires significant customization, such as dedicated topics per pair of devices, which increases the complexity and overhead of the system [12].”
Feature Skew Control for In-Network Federated Learning
Chaouche, Y., Ntumba, P., Secci, S., et al. (2025) · International Conference on Network and Service Management (CNSM) · doi:10.23919/cnsm67658.2025.11297468
“This trend goes beyond basic centralized learning, to enable the deployment of in-network AI services at scale [3]; indeed, transmitting a huge volume of sensitive data to remote clouds over the network raises significant privacy and network efficiency concerns: privacy ones gave rise to Federated…”
QR-MO: Q-Routing for Multi-Objective Shortest-Path Computation in 5G-MEC Systems
Sarah, A., Garroppo, R. G., & Nencioni, G. (2025) · International Conference on Network and Service Management (CNSM) · doi:10.23919/cnsm67658.2025.11297491
“However, machine learning methods for multi-objective optimization, including RL, have advanced broadly without sufficient attention to edge computing’s unique constraints [15].”
Joint Request Batching and Worker Assignment in AI-RAN Edge Inference Systems
Qiu, N., Gan, S., Wang, X., Feng, C., Chen, X., Han, S., & Wang, X. (2025) · International Conference on Future Communications and Networks (FCN) · doi:10.1109/fcn66513.2025.11296658
“Previous research [1] has highlighted the advantages of deploying LLM models on edge nodes, but also pointed out the challenges of executing compute-intensive inference tasks under resource-constrained conditions.”
Self-citationAdaptive Collaborative DNN Inference via Heterogeneous Graph Attention in Edge Environments
Shi, C., Jiang, B., Xu, H., Xie, J., & Wang, C. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3648047
“Surveys [27], [28] reported that such optimizations remain lacking in environments characterized by device heterogeneity and workload variability.”
Karahan, S. N., Güllü, M., Osmanca, M. S., & Barışçı, N. (2025) · Future Internet · doi:10.3390/fi18010017
A Unified Framework for Task-Oriented Communication at Edge AI
Balevi, E. (2026) · IEEE Transactions on Communications · doi:10.1109/tcomm.2025.3650276
“To illustrate, the advanced driver-assistance system (ADAS) may employ edge AI for multiple tasks, e.g., vehicle and pedestrian detection, and traffic sign recognition [5].”
The Future of Banking: Leveraging AI for Business Transformation
Seranmadevi, R., Pavan Kumar Raju., R., R., A. P., & Sinha, S. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-87372-0_29
Chiranjeevi, M., & Chavan, A. (2026) · Cognitive science and technology · doi:10.1007/978-981-95-0140-3_1
Real-Time Indoor Temperature Forecasting Using Edge Computing in Free-Running Offices
Gustin, M., Hopfe, C. J., Kohl, T., et al. (2026) · Lecture notes in civil engineering · doi:10.1007/978-3-032-06810-1_84
TIMISICĂ, D., BONCEA, R. M., Balmus, S., et al. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-12481-4_8
Dynamics-Based Adaptive Scheduling for Large-Scale Edge Computing Networks under Dynamic Scenarios
Feng, Y., Chen, Y., Ding, Y., et al. (2025) · 2022 IEEE 20th International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin64977.2025.11279695
“Traditional optimization algorithms perform well in static scenarios but suffer from slow convergence in dynamic environments, making it difficult to meet real-time requirements [9].”
Wang, X., & Di, B.-Y. (2025) · 2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall) · doi:10.1109/vtc2025-fall65116.2025.11310298
“Unlike traditional edge-cloud collaboration, which distributes identical tasks across users for workload balancing [8], this framework necessitates tailored strategies for edge-side data uploading and cloud-side model updates.”
vTensor-Based GPU Memory Management for Edge Deep Learning Training
Gao, H.-R., Luo, D.-H., Wu, Y., Wu, H., & Zhang, W.-B. (2025) · Journal of Computer Science and Technology · doi:10.1007/s11390-025-4788-2
A Serverless Quantization-as-a-Service Model to Run Compression Jobs for Edge Intelligence
Novi, D. D., Dell’Acqua, P., Carnevale, L., et al. (2025) · Proceedings - IEEE Symposium on Computers and Communications/IEEE Symposium on Computers and Communications · doi:10.1109/iscc65549.2025.11326460
“At the highest level, both training and inference occur entirely on the device itself [34].”
InfluentialGanesh, G., Nasreen, M. A., & Jaganathan, A. (2025) · International Conference on Automation, Computing and Renewable Systems (ICACRS) · doi:10.1109/icacrs67045.2025.11324399
“When the AI is employed along with security-oriented solutions such as blockchain and federated learning, trust can even be achieved with respect to data integrity, privacy, and trust among stakeholders [9][10].”
CLF-SFC: Freshness-Aware Service Function Chain Orchestration Across End-Edge-Cloud
Cheng, W., Wang, X., Yi, B., Lu, X., Zhao, Y., Zhang, C., & Huang, M. (2025) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads67057.2025.11323119
“Edge intelligence bridges sensing/compute across the end–edge–cloud continuum [14].”
Sekhar, K. C., Varshini, D. V. D. S., Lakshmi, S. G. N. S., & Yogi, M. K. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-4277-1.ch007
The role of optimization techniques in achieving sustainable artificial intelligence
Hussain, H., & Tamizharasan, P. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-32878-7.00008-0 · Cited by 1
An All-Reduce Compatible Top-$K$ Compressor for Communication-Efficient Distributed Learning
Chen, C., Ma, C., Li, Z., et al. (2025) · doi:10.1109/cloudcom67567.2025.11331331
“Their key property is that the compression error diminishes in expectation as the underlying variable approaches zero.”
Energy-Efficient Inference Task Scheduling in Multi-Site Collaborative Systems
Zhang, X., Gui, M., Liu, H., et al. (2025) · International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) · doi:10.1109/aeeca65693.2025.00127
Edge-Enhanced YOLOV8 for Spacecraft Instance Segmentation in Cloud-Edge IoT Environments
Chen, M., Chen, W., Niu, Y., Qi, P., & Wang, F. (2026) · Future Internet · doi:10.3390/fi18010059
“A key enabler of this paradigm is edge intelligence, which involves deploying lightweight AI models directly on edge devices [7].”
Computation-bandwidth-memory trade-offs: a unified paradigm for AI infrastructure
Fan, Y., Weng, Q., & Li, X. (2026) · Vicinagearth. · doi:10.1007/s44336-026-00041-4 · Cited by 4
“These approaches demonstrate how strategic use of bandwidth-memory trade-offs improves the efficiency of model training.”
A Lightweight Framework for Adaptive VisionModel Inference Across Edge and Cloud Devices
Wang, D., Pan, J., Liang, F., Zeng, Y., Zhou, Y., & Fan, X. (2025) · IEEE International Conference on Cloud Computing Technology and Science · doi:10.1109/cloudcom67567.2025.11331467
“With the flourishing development of edge applications such as autonomous vehicles, intelligent surveillance, and wearable devices, there are increasingly higher demands for low-latency, privacy-preserving inference capabilities [12].”
Jurczak, M., Marchut, P., Maj, P. G., et al. (2026) · Journal of Instrumentation · doi:10.1088/1748-0221/21/01/c01028
“In particular, there is a growing demand for solutions that integrate intelligent real-time data processing directly on the detector, enabling more efficient characterization and analysis of complex signals [5, 6].”
Mahalakshmi, M. S., Veena, K., Pandi, V. S., Samiappan, D., Karthi, K., & Ali, G. (2025) · International Conference on Smart Structures and Systems (ICSSS) · doi:10.1109/icsss66939.2025.11346433
Lee, J., Shin, Y.-J., & Kang, S. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11343299
“For instance, Zhou et al. [10] proposed a six-level rating system specifically designed for edge intelligence, which provided structured insights into distributed architectures.”
Transfer Reinforcement Learning for Resource Allocation in Mobile Edge Computing Systems: A Survey
Ni, C., Kong, X., Du, J., Shen, G., Al-Asad, W., & Min, G. (2026) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2026.3658306 · Cited by 1
Toward a Scalable Video Surveillance System Using Containerization and Object Storage Technologies
Nguyen-Thi, H.-T., Nguyen, T.-H., Do, T.-L., et al. (2026) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom69009.2026.11360860
“Studies by Zhou et al. [2], Patil and Wagh [3], and Kumar et al. [4] have demonstrated that processing video streams closer to the edge can significantly reduce latency and alleviate network bandwidth pressure.”
Kumar, A., Kaur, S., Sachdeva, R., & Kaur, K. (2025) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat66879.2025.11362512
“Edge Computing’s growth necessitates lightweight, scalable IDS models that combine advanced learning with privacy preservation and explainability [17, 18].”
An In-model Spy in Edge Intelligence
Yang, F., Chen, P., Yan, Y., & Yang, Z. (2025) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1109/raid67961.2025.00023
“With the proliferation of edge devices, the capability of aggregating their data and analyze them into useful insights is essential for achieving the vision of smart home and smart industry [1].”
Zhang, H., Yu, W.-H., Zhao, Z., et al. (2026) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2026.3656981
“T HE increasing demand for intelligent processing has driven the rapid proliferation of machine learning (ML)-based sensor nodes, which have demonstrated remarkable capabilities in analyzing complex environmental and physiological data patterns at the edge [1].”
An Experimental Study of Application of Edge AI for Accelerated Computation
Banerjee, S., Ganguly, G., Kumar, S., et al. (2025) · doi:10.1109/iemecon69302.2025.11365818
“With a concern, Industrial Internet of Things (IoT) systems utilizing edge AI attained 60% data transmission volume reduction with less network congestion and enhanced reliability [4].”
Energy-Efficient Cooperative Inference in Multidevice Edge Networks: A Lyapunov-Based Approach
Lee, K., & Choi, H.-H. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3660845 · Cited by 1
“The edge device performs initial computations and forward intermediate outputs to the server, which then completes the inference process [13].”
Frugal Ontology Alignment for WoT Servients
Dahmani, M., Bento, A., Ayadi, N. Y., et al. (2026) · doi:10.1109/ccnc65079.2026.11366392
“The deployment of AI capabilities on resource-constrained devices emerged as a research area [10].”
Personalizing Learning Through AI-Driven Educational Technologies
Pandey, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9195-3.ch005
Adaptive Federated Learning for energy-aware wireless edge networks in 6G environments
Mohammed, B. (2026) · Computer Communications · doi:10.1016/j.comcom.2026.108452 · Cited by 3
Zhao, H., Li, Y., Liu, P., Wang, W., Shi, L., & Cui, Y. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3661975
Enhancing Attention-Based Visual Processing With Noise-Boosted Activation Functions
Gu, X., Ren, Y., Duan, F., & Abbott, D. (2026) · IEEE Access · doi:10.1109/access.2026.3663468
Dolphin: an adaptive lossless compression algorithm for oscillating floating-point time series
Chen, W., Yan, L., Jung, H., & Zhao, H. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00553-5 · Cited by 1
Cybersecurity Threats Prevention through PUF Based Dynamic Logic Morphing on IoT Edge Devices
Dey, T. K., & Hossain, F. S. (2025) · doi:10.1109/compas67506.2025.11381648
“In parallel, edge AI is being employed to empower devices with real time decision making capabilities, minimizing dependence on centralized infrastructure [25], [26].”
Adaptive Batch Processing and Resource Allocation for LLM Inference in Edge Computing
Yang, L., Gan, S., Wang, X., Liu, J., & Chen, X. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-14681-6_4
Self-citationEdge AI for Industry 5.0: Challenges and Opportunities
Medojević, M., Tošić, M., Bajić, B., & Rikalović, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-04890-5_6
Neural Network-based Predictive Models for Accurate Crop Yield Forecasting
Sharma, Y. R., Ch, S. R., Bojja, S., et al. (2025) · doi:10.1109/icecmsn68058.2025.11383517
Performance and Cost Optimization of Federated LLM Agents in Edge Computing Environments
Tadi, G., & Mittal, A. (2025) · International Conference on Foundation and Large Language Models (FLLM) · doi:10.1109/fllm67465.2025.11391052 · Cited by 1
“These interconnected constraints require holistic optimization [1], [13], [18].”
InfluentialQuantum-Enhanced Edge Intelligence: Bridging Quantum Computing and Distributed AI
Tran‐Dang, H., & Kim, D. (2025) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc66702.2025.11389001 · Cited by 1
“This reduces communication overhead, enhances privacy, and enables timely responses in critical applications such as collision avoidance and medical diagnostics [6].”
Marwala, T. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36322-1.00008-0
5G Network Edge Intelligence for Smart Operation and Maintenance of Offshore Wind Power
Gao, Y., Yang, L., Zhu, X., Jiang, C., Wang, H., You, S., & Xu, F. (2026) · Sensors · doi:10.3390/s26041390
“Additionally, small-footprint DNN frameworks have shown latency improvements of 5–10 × by using hash codes, and specific architectural optimizations can achieve an immediate inference speedup of 20% [6,12].”
InfluentialCombinatorial Double Auction for UAV-Assisted Smart City Applications in Edge Intelligence
Ng, J. S., Leung, C., Niyato, D., & Miao, C. (2025) · IEEE International Conference on Smart City and Informatization (iSCI) · doi:10.1109/isci66495.2025.00009
Stochastic Transformation Based Error Reduction for Image Processing Application
Chowdhury, A., Biswas, R. N., Mitra, S. K., et al. (2025) · doi:10.1109/aspcon66877.2025.11389388
“Edge Computing have become important for AI [5].”
Greedy Low-Rank Gradient Compression for Distributed Learning With Convergence Guarantees
Chen, C., He, Y., Li, P., et al. (2026) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2026.3667669
“In addition to its applications in edge computing [1], [2], [3], federated learning [4], [5], [6], and robotic control [7], distributed optimization is particularly useful for training deep neural networks [8], [9], [10], [11].”
Energy-Efficient and Low-Latency Convergent Split Learning in Artificial Intelligence of Things
Li, F., Xiao, Y., Wei, Y., et al. (2025) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct67417.2025.11374001
“The Artificial Intelligence of Things (AIoT) integrates artificial intelligence (AI) technologies with the Internet of Things (IoT) infrastructure, enabling smart sensing, real-time analytics, and autonomous decision-making in distributed environments [1].”
Vishwakarma, M. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-6415-5.ch007
A Privacy-Preserving Cybersecurity Framework for AI-Driven Green Mobility Ecosystems
Abreu, R., Sousa, A. V., Correia, L., et al. (2025) · doi:10.1109/icecer65523.2025.11401363
“To address these limitations, researchers are increasingly proposing hybrid and decentralized approaches, including edge computing and federated learning, which enable real-time threat detection while preserving data privacy [6], [7].”
Zia, M. F., Ouameur, M. A., Bagaa, M., Massicotte, D., & Ksentini, A. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112142 · Cited by 3
Transformation, Analysis, and Storage of Real-Time Stock Data: An Edge Computing Approach
Pillai, N. V., & Nitnaware, P. P. (2025) · doi:10.1109/delcon68055.2025.11400108
“Recent studies have shown the advantages of edge-based architectures in applications involving high-frequency trading, fraud detection, and micro-pattern discovery [3], [4].”
Picano, B., Paolieri, M., Carnevali, L., et al. (2026) · Performance Evaluation · doi:10.1016/j.peva.2026.102556
“In this ecosystem, edge nodes act as intermediaries, handling latency-sensitive and computation-intensive tasks before offloading residual workloads to the cloud for large-scale optimization [1, 2, 3, 4, 5].”
Large model–driven edge-cloud collaboration for next-gen animation systems
Wang, C., Zhu, J., & Huang, W.-J. (2026) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-026-00870-0
Optimizing EdgeML Image Denoising with PnP-ADMM, Quantization, and Pruning
Edwards, M. A., & Kumar, L. K. S. (2025) · doi:10.1109/reacs67479.2025.11413359
“The proposed framework enables efficient image denoising on edge devices, achieving a model size of 0.06 MB and latency of 0.0080 seconds, ideal for IoT sensors and embedded systems [1].”
InfluentialMole, M. (2026) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.5000
“For example, DNN splitting and on-device processing are used to send only partially processed, non-sensitive features instead of raw data [1].”
InfluentialEdge-AI for Adversarial IoT: A Resilient Framework for Mission-Critical Intelligence
Goyal, K. K. (2025) · International Conference of Science and Information Technology in Smart Administration (ICSINTESA) · doi:10.1109/icsintesa68165.2025.11413720
“Localizing AI also introduces new design problems, particularly when it comes to resilience, adaptability, and explainability—issues that are compounded in more volatile and hostile environments [1], [2].”
Roosan, D., Hai, F., Nirzhor, S., et al. (2026) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon64486.2026.11418875
Sharma, G., Kumar, A., Gupta, N., Bhargava, S., & Tomar, J. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-31424-7.00013-7
Kassekeyeva, A. B., Tokhmetov, A., Kenesbai, M., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6412851
Siregar, A. A. P., Novian, Z., Pradana, M. R. A. A., Maryamah, L., & Novita, N. (2026) · Mantiqu Tayr Journal of Arabic Language · doi:10.25217/mantiqutayr.v6i1.7433
Systems, Networks, and Digital Communication Technologies: A Comprehensive Review
Mohamed, T. S. (2025) · GlobeIS International Journal of Global Information Systems · doi:10.66395/globeis.1
“…research work in communication networks should emphasize: • Edge AI: Efficient on-device learning and inference for latency-critical applications [121] • Explainable AI (XAI): Interpretable models for regulation and trust [122] • Some of the recent works that have focused on Non-Euclidean…”
Edge and Cloud-Based AI Inference for Big Data Processing
Elmobark, N. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7046-0.ch004
Quantum computing for edge AI: opportunities, challenges, and future research directions
Tran‐Dang, H., & Kim, D. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08436-2
Lin, Z., Chen, W., Zeng, D., et al. (2026) · doi:10.1109/emc68537.2026.11441669
“Edge computing principles further support system deploy-ability by placing inference operations closer to the point of data acquisition, thereby reducing end-to-end latency and communication cost [23].”
Soni, H., Kunthu, B. R., Chincholi, T. V., et al. (2025) · IEEE Global Conference on Wireless Computing and Networking (GCWCN) · doi:10.1109/gcwcn66157.2025.11448518
“The architectural goal is not simply to detect anomalies but to provide actionable, context-rich insights with minimal latency [2].”
Metadata
- Authors
- Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J.
- Affiliations
- Sun Yat-sen University (CN)
- Arizona State University (US)
- Venue
- Proceedings of the IEEE
- Published
- 2019-08
- Volume
- 107
- Issue
- 8
- Pages
- 1738-1762
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2950865323
- Semantic Scholar
- 928cd808aba140ec298508df87c5579811ff2f41
- Cited by
- 2,455
- References
- 168
- Citations by year
- 2026: 455 · 2025: 352 · 2024: 332 · 2023: 363 · 2022: 370 · 2021: 341 · 2020: 215 · 2019: 26
- FWCI
- 130.23
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the IEEE (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Basic Research Program of China (2017YFB1001703)
- National Natural Science Foundation of China (U1711265, 61802449)
- Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2017ZT07X355)
- Guangdong Natural Science Funds (2018A030313032)
- Fundamental Research Funds for the Central Universities (17lgjc40)
- Army Research Office (W911NF-16-1-0448)
- Defense Threat Reduction Agency (HDTRA1-13-1-0029)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- edge AI, edge computing, edge computing architecture, deep learning training, deep learning inference, Internet of Things, mobile computing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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