The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index; 1 of the 25 sources its claims cite were checked for support.
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
Open accessAbstract
Recently, along with the rapid development of mobile communication technology, edge computing theory and techniques have been attracting more and more attention from global researchers and engineers, which can significantly bridge the capacity of cloud and requirement of devices by the network edges, and thus can accelerate content delivery and improve the quality of mobile services. In order to bring more intelligence to edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with mobile edge systems, for optimizing mobile edge computing, caching and communication. And thus, we design the “In-Edge AI” framework in order to intelligently utilize the collaboration among devices and edge nodes to exchange the learning parameters for a better training and inference of the models, and thus to carry out dynamic system-level optimization and application-level enhancement while reducing the unnecessary system communication load. “In-Edge AI” is evaluated and proved to have near-optimal performance but relatively low overhead of learning, while the system is cognitive and adaptive to mobile communication systems. Finally, we discuss several related challenges and opportunities for unveiling a promising upcoming future of “In-Edge AI.”
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper proposes “In-Edge AI,” integrating Deep Reinforcement Learning and Federated Learning into mobile edge systems to optimize edge caching and computation offloading. It reports proof-of-concept simulations where DDQN with FL approaches centralized DDQN performance while reducing wireless transmission cost.
Key findings
- In edge caching, DDQN with FL achieved hit rates close to centralized DDQN for three edge nodes (E1, E2, E3) and outperformed LRU, LFU, and FIFO.
- In computation offloading, DDQN with FL achieved near centralized DDQN performance and was superior to mobile execution, edge node execution, and greedy execution for three UEs (m1, m2, m3).
- Centralized DDQN performed better at the beginning of training, but after several FL model merges, DDQN with FL became near to centralized DDQN.
- FL clients upload only model updates, reducing total wireless transmission compared with centralized DRL, where UEs must upload the whole training data.
Methods
- Two simulations were run: edge caching and computation offloading. Edge caching used Xender’s one-month trace (01/09/2016 to 30/09/2016) with 9,514 active mobile users, 188,447 content files, and 2,107,100 content requests, fitted by a Zipf distribution with ∂ = 1.58; 6 edge nodes cooperated, coordinated by a cloud server.
- Computation offloading assumed 5 MHz bandwidth divided into 10 wireless channels, 10 UEs as FL clients, channel gain quantized into 6 levels, and tasks generated by Bernoulli distribution with average arrival rate λ_T per time epoch.
- DRL used Double DQN with tanh activation and Adam optimizer; a single-layer fully-connected feedforward neural network with 200 neurons served as MainNet and TargetNet. Parameters: replay memory M = 5000, minibatch B = 200, discount γ = 0.9, exploration ε = 0.001, learning rate ζ = 0.005, target replacement period φ = 250. A centralized DRL baseline was assumed to receive all data.
Limitations
- The paper states FL cannot outperform centralized DL, only achieve near-best performance, and that FL trades advantages for heavier client computation load and more energy cost or burden on UEs and edge nodes; these remain open questions.
- The paper states privacy and security are not its focus, and that In-Edge AI is future-oriented: state-of-the-art edge chips support only elementary DL training, deploying a randomly initialized DRL model could paralyze the MEC system, and delay requirements remain challenging; transfer learning is proposed to alleviate training.
- The paper states the training-process comparison assumes an ideal wireless environment where massive centralized training data and lightweight FL updates can be uploaded within a discretized time epoch, whereas in practice massive training data cannot be uploaded without delay. It also assumes slowly changing content popularity, equal content sizes, and static popularity, user preference, and average request arrival rates over a relatively long period. Evident from the text: evaluation is simulation-based proof-of-concept, not a real-world deployment.
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32 Own interpretation · 17 Not checked yet · 9 Own result · 2 Sources not found · 2 No basis given · 1 Partly supported · accuracy not yet measured
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the perspective of edge caching, E 1 , E 2 and E 3 are chosen to cache contents, and hit rates of them are improving and finally maintain within a certain range along with the process of training DRL agents.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Without FL framework, viz., using centralized DRL, UEs must upload the whole training data via wireless channels and thus consume more communication resources, which is demonstrated by Fig. 6(d).”
boosteddirect- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: In the framework of FL, every client only needs to upload the update of its model.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We perform experiments on investigating the scenarios of edge caching and computation offloading in mobile edge system, and the 'In-Edge AI' is evaluated and proved to have the abilities to achieve near-optimal performance.”
boosteddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“We capture Xender's trace for one month (from 01/09/2016 to 30/09/2016), including 9,514 active mobile users, conveying 188,447 content files, and 2,107,100 content requests [13], of which the content popularity distribution can be well fitted by a Zipf distribution with ∂ = 1 . 58 .”
What the sources say
- Not matchedcites [13] [13] X. Li, X. Wang, P.-J. Wan, Z. Han, V. C.M. Leung, 'Hierarchical Edge Caching in Device-to-Device Aided Mobile Networks: Modeling, Optimization, and Design', IEEE Journal on Selected Areas in Communications , Special Issue on Caching for Communication Systems and Networks, Early Access, 2018. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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A result the paper reports from its own data — 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, once the model merging of FL has been processed several times, the performance of DDQN with FL becomes near to that of centralized DDQN.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“As illustrated in Fig. 6(c), the performance of centralized DDQN is better than that of DDQN with FL at the beginning of training.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“In a word, the problem existed in the resource allocation optimization of the MEC system is ' lack of intelligence '.”
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In the paper
“2) reacting cognitively to the mobile communication environment and conditions of cellular networks”
What the sources say
- Not checked yetcites [11] [11] H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, 'Communication-Efficient Learning of Deep Networks from Decentralized Data,' in Proceedings of the International Conference on Artificial Intelligence and Statistics , Apr. 2017. - source
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In the paper
“3) adapting well with heterogeneous UEs in a practical cellular network”
What the sources say
- Not checked yetcites [11] [11] H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, 'Communication-Efficient Learning of Deep Networks from Decentralized Data,' in Proceedings of the International Conference on Artificial Intelligence and Statistics , Apr. 2017. - source
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In the paper
“2) Dynamic Conditions: dynamics of the integrated communication and computation system are not well addressed;”
What the sources say
- Not checked yetcites [3] [3] M. Chen and Y. Hao, 'Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network,' in IEEE J. Sel. Areas Commun. , vol. 36, no. 3, pp. 587-597, Mar. 2018 -
- Not checked yetcites [4] [4] Y. Mao, C. You, J. Zhang, K. Huang, K. B. Letaief, 'A Survey on Mobile Edge Computing: The Communication Perspective', in IEEE Commun. Surv. Tutorials , vol. 19, no.4, pp. 2322-2358, Aug. 2017. - source
plain- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 25
- Probably not a substantial claim. 21
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In the paper
“But [7] is based on Q-Learning, which is not feasible for the practical MEC system where the stateaction space is tremendous”
What the sources say
- Partly supportedcites [7] [7] A. Sadeghi, F. Sheikholeslami, and G. B. Giannakis, 'Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities,' in IEEE J. Sel. Top. Signal Process. , vol. 12, no. 1, pp. 180-190, Feb. 2018. - sourceExperimental — accuracy not yet measured
Section IV (p. 6) says standard Q-learning faces practical challenges because its Q-table grows prohibitively large and action selection requires an expensive search. But Algorithm 2 (p. 7) presents a scalable Q-learning approximation designed to handle the large state-action space. The claim is supported for the paper’s exact Q-learning algorithm, not for its proposed approach as a whole. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“'In-Edge AI' is evaluated and proved to have near-optimal performance but relatively low overhead of learning, while the system is cognitive and adaptive to the mobile communication systems.”
- Also stated in: § I. INTRODUCTION · p. 2 · plain
“we provide proof-of-concept evaluation and verify that the proposed scheme has advantages on the balance of performance and cost.”
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It may state two separate claims as one.
- Also stated in: § I. INTRODUCTION · p. 2 · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It can be easily seen that the specific performance of FL is near close to the results of centralized DDQN in terms of achieved hit rates of 3 clients (edge nodes), and outperform LRU, LFU, and FIFO.”
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the simulation of computation offloading, the average utilities of three UEs m 1 , m 2 and m 3 are increasing with the decreasing of training loss, and also maintain within a certain range at last.”
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In Fig. 6(b), it can be observed that the DDQN with FL achieves the near performance of centralized DDQN and is superior to another three baseline policies;”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Certainly, due to the limitation of federated optimization, FL could not outperform the centralized DL trained model, but could still achieve the near best performance, which will demonstrated in Section IV.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“edge nodes, i.e., base stations equipped with computation/storage capability, could deal with the computation and content requests of UEs, and consequently this scheme improves the Quality-of-Service (QoS) of Mobile Network Operators (MNOs) and the Quality-ofExperience (QoE) of UEs and relieves the load of backbone networks, and pressure of clouds (data centers) [2].”
What the sources say
- Not checked yetcites [2] [2] X. Wang, M. Chen, T. Taleb, A. Ksentini, V. C. M. Leung, 'Cache in the Air: Exploiting Content Caching and Delivery Techniques for 5G Systems,' in IEEE Communications , vol. 52, no. 2, pp. 131-139, Feb. 2014. -
hedgeddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Federated Learning [11] is introduced as a framework for training DRL agents in a distributed manner while 1) largely reducing the amount of data which should be uploaded via the wireless uplink channel”
What the sources say
- Not checked yetcites [11] [11] H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, 'Communication-Efficient Learning of Deep Networks from Decentralized Data,' in Proceedings of the International Conference on Artificial Intelligence and Statistics , Apr. 2017. - source
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“MEC circumvents the long propagation delays introduced by transmitting data from mobile devices to remote cloud computing infrastructures, and hence is able to support latency-critical mobile and Internet of Things (IoT) applications.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“3) Temporal Isolation: most of them do not consider the longterm effect of current decision on resource allocation except for Lyapunov optimization, viz., in a highly time-varying MEC system, most of proposed optimization algorithms is optimal or close-to-optimal only for a snapshot of the system.”
What the sources say
- Not checked yetcites [3] [3] M. Chen and Y. Hao, 'Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network,' in IEEE J. Sel. Areas Commun. , vol. 36, no. 3, pp. 587-597, Mar. 2018 -
- Not checked yetcites [4] [4] Y. Mao, C. You, J. Zhang, K. Huang, K. B. Letaief, 'A Survey on Mobile Edge Computing: The Communication Perspective', in IEEE Commun. Surv. Tutorials , vol. 19, no.4, pp. 2322-2358, Aug. 2017. - source
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
“the emerging idea of Mobile Edge Computing (MEC) is proposed [1] as a novel paradigm for easing the burden of backbone networks by pushing the computation/storage resources to the proximity of the User Equipments (UEs).”
What the sources say
- Not matchedcites [1] [1] ETSI, 'Mobile Edge Computing - Introductory Technical White Paper', Sep. 2014. -
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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In the paper
“none of these works has thought over 1) in what form the training data shall be gathered (whether in a distributed or a centralized way), 2) where the reinforcement learning agent should be placed and trained (whether in UEs, edge nodes or remote cloud infrastructures), 3) how the update process of reinforcement learning agents should be proceeded and collaborated, and 4) the privacy protection of”
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None of the sources cited for this claim have been checked yet.
In the paper
“a recent new trend is to optimize wireless communication by Artificial Intelligence (AI) techniques [4] [5], include but not limited to the application of AI for Physical Layer (PHY), Data Link Layer, and traffic control [6].”
What the sources say
- Not checked yetcites [4] [4] Y. Mao, C. You, J. Zhang, K. Huang, K. B. Letaief, 'A Survey on Mobile Edge Computing: The Communication Perspective', in IEEE Commun. Surv. Tutorials , vol. 19, no.4, pp. 2322-2358, Aug. 2017. - source
- Not checked yetcites [5] [5] Q. Mao, F. Hu, and Q. Hao, 'Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey,' in IEEE Commun. Surv. Tutorials , Early Access, 2018. - source
- Not checked yetcites [6] [6] F. Tang, B. Mao, Z. M. Fadlullah, N. Kato, O. Akashi, T. Inoue, and K. Mizutani, 'On Removing Routing Protocol from Future Wireless Networks: A Real-Time Deep Learning Approach for Intelligent Traffic Control,' in IEEE Wireless Communications , vol. 25, no. 1, pp. 154-160, Feb. 2018. - source
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
“the focus of [8] is on vehicular networks.”
What the sources say
- Not checked yetcites [8] [8] Y. He, N. Zhao, and H. Yin, 'Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach,' IEEE Trans. Veh. Technol. , vol. 67, no. 1, pp. 44-55, Jan. 2018. -
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
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
“1) the computation capability of a UE is relatively weak, and it will consume long time or is even impossible to train the DRL agent on large-scale data;”
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The computation capability of a UE is relatively weak.
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 will cause more energy cost on UEs or burden the computation of both UEs and edge nodes, and these issues are still open questions.”
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.
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
“Interestingly, this is the reverse direction of the aforementioned Federated Learning, but also a kind of application of the 'In-Edge AI'.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no 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
“3) if training data is transformed for privacy consideration, server-side proxy data is less relevant than on-device data.”
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: If training data is transformed for privacy, server-side proxy data is less relevant than on-device data.
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
“Obviously, the DRL model should not be deployed directly while setting weights of neural networks at random. Otherwise, the MEC system will be paralyzed because the DRL model could only make random decisions at preliminary exploration.”
hedgeddirect- 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
“By incorporating this work, the requirement on computation capacity of UEs could be relaxed along with decreasing the inferring delay.”
What the sources say
- Not checked yetcites [14] [14] E. Li, Z. Zhou, X. Chen, 'Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy,' in ACM SIGCOMM, MECOMM Workshop , 2018. -
hedgeddirect- Asserted: passes — The authors put it forward as true.
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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
“Nevertheless, using additional computation could decrease the consumption of communication rounds needed to train a model. In addition, FL only asks a part of clients, in one round, to upload their updates, which handles the circumstance where clients are often unpredictably off-line.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: UEs are often unpredictably off-line or allocated with poor communication resources.
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
“each edge node can support AI tasks on system level dynamically, not only for its own but for the global optimization and balancing of the whole MEC system.”
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“However, most distributed DRL architectures, described by Fig. 4(b), could not handle unbalanced and non-IID data and cope with the privacy issues [11].”
What the sources say
- Not checked yetcites [11] [11] H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, 'Communication-Efficient Learning of Deep Networks from Decentralized Data,' in Proceedings of the International Conference on Artificial Intelligence and Statistics , Apr. 2017. - source
hedged- Asserted: passes — The authors put it forward as true.
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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
“With the aid of FL framework, edge nodes equipped with AI computation in 'edge' could combine the cloud and massive UEs together and form a powerful AI entity with strong cognitive ability provided by massive UEs and edge nodes.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“The information needs to be uploaded for FL is the minimal update necessary to improve the DRL agent. Further, techniques of secure aggregation and differential privacy could be applied naturally, which could avoid that privacy-sensitive data are contained in local updates.”
What the sources say
- Not checked yetcites [11] [11] H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, 'Communication-Efficient Learning of Deep Networks from Decentralized Data,' in Proceedings of the International Conference on Artificial Intelligence and Statistics , Apr. 2017. - source
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- 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
“2) the training data which should be uploaded to edge nodes or cloud is privacy-sensitive, and it might cause potential privacy accidents;”
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: 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: Training data that should be uploaded to edge nodes or cloud is privacy-sensitive.
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
“Based on the pre-established DRL, the requirement of training (not inferring) is alleviated, i.e., the DRL agent could reach the satisfied accuracy level after a small number of mini-batches or even directly inferring.”
hedgeddirect- 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. It may state two separate claims as one.
- Rests on an assumption: Learning takes long time of training as well as inferring according to the required accuracy level.
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
“And when the wireless environment or the pattern of UEs' requests is changed, the UE could also take advantage of the established DRL to adjust neural networks in its DRL model.”
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.
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
“Specifically, powerful (both in computation and energy) UEs could decide to perform more mini-batches in training for further decreasing the communication cost and vice versa.”
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. It may state two separate claims as one.
- Rests on an assumption: In FL, additional computation could be used to decrease the number of communication rounds to train a model.
- Rests on an assumption: An effective way to add computation is increasing computation per UE, i.e., adding more local SGD updates per round.
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
“Consequently, the performance of In-Edge AI shall not be easily affected by the unbalanced data and sometimes the poor communication environment.”
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. It may state two separate claims as one.
- Rests on an assumption: FL inherently could address key issues about the availability of UEs and edge nodes and the unbalanced and non-IID data.
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, if using transfer learning, there shall be less time or computation consumption on the side of training.”
plainconditional- 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.
- Rests on an assumption: Learning takes long time of training as well as inferring according to the required accuracy level.
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
“Nonetheless, this may be solved if the DL model is not trained from scratch.”
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.
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
“In-Edge AI is a tight confederation among mobile operators, SPs/CPs, and mobile users, while capable entities may contribute more to the overall optimization of the edge caching and computing tasks, but a large number of UEs may rely on the AI of edge nodes and other UEs.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check 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. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no 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
“DRL can be treated as a type of specialized edge computing task at the service-level.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check 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. It may be too vague to check against evidence. 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
“Then, the established DRL model could be distributed to mobile UEs.”
hedged- 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
“Further, its ability of handling non-IDD data allows massive UEs in different wireless environments to train their own DL model without concerning about the negative effect.”
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.
- Rests on an assumption: FL inherently could address key issues about the availability of UEs and edge nodes and the unbalanced and non-IID data.
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
“Specifically, due to the fact that the coordinating server in FL only executes tasks of merging updates instead of taking over the whole training, the computation load of clients is inevitably heavier on account of the local training process.”
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: 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.
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
“though keeping and training a DRL agent in every UE and edge node can achieve the best performance, it is only practical to apply distributed DRL in the MEC system, because there shall not have enough time and data for 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: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
- Rests on an assumption: There is not enough time and data for training a DRL agent in every UE and edge node.
The paper relies on this without support.
A typical 5G node is expected to have 2000 or more configurable parameters.
“a typical 5G node is expected to have 2000 or more configurable parameters”
The authors state this as an assumption.
Content popularity changes slowly and all contents have the same size.
“we assume that the content popularity changes slowly and all contents have the same size.”
The authors state this as an assumption.
All content popularity, user preference and average arrival rate of requests are static during a relatively long period.
“We assume that all content popularity, user preference and average arrival rate of requests are static during a relatively long period.”
The authors state this as an assumption.
Edge node computing performance is much stronger than the UE's, so f_E is much greater than f_L.
“the formula f E ≫ f L holds (the computing performance of edge nodes is much stronger than the UE's).”
The paper relies on this without support.
Training data is large in quantity when considering massive UEs.
“the training data is large in quantity when considering massive UEs”
The paper relies on this without support.
Training data that should be uploaded to edge nodes or cloud is privacy-sensitive.
“the training data which should be uploaded to edge nodes or cloud is privacy-sensitive”
The paper relies on this without support.
If training data is transformed for privacy, server-side proxy data is less relevant than on-device data.
“if training data is transformed for privacy consideration, server-side proxy data is less relevant than on-device data.”
The paper relies on this without support.
The computation capability of a UE is relatively weak.
“the computation capability of a UE is relatively weak”
The paper relies on this without support.
The training process of a DRL agent may introduce extra energy consumption of a UE.
“the training process of a DRL agent may introduce extra energy consumption of a UE.”
The paper relies on this without support.
DRL techniques require intensive computation capacity for finding optimal solutions.
“DRL techniques require intensive computation capacity for finding the optimal solutions.”
The paper relies on this without support.
There is not enough time and data for training a DRL agent in every UE and edge node.
“there shall not have enough time and data for training.”
The paper relies on this without support.
UEs are often unpredictably off-line or allocated with poor communication resources.
“UEs are often unpredictably off-line or allocated with poor communication resources.”
The paper relies on this without support.
Some UEs may have more computation tasks and some may experience more states of mobile networks, resulting in varying amounts of training data among UEs.
“Some UEs may have more computation tasks to be handled and some may experience more states of mobile networks, resulting in varying amounts of training data among UEs.”
The paper relies on this without support.
FL distributes the quality of knowledge across a large number of devices without necessarily centralizing the training data.
“The core benefit of FL lies in distributing the quality of knowledge across a large number of devices without necessarily centralizing the training data.”
The paper relies on this without support.
Server-side proxy data is less relevant than on-device data.
“Server-side proxy data is less relevant than on-device data.”
The paper relies on this without support.
In the MEC system, massive UEs and edge nodes could act as perceptrons and acquire various, abundant and personalized data for updating the global DL model.
“In the MEC system, massive UEs and edge nodes could act as perceptrons and acquire various, abundant and personalized data for updating the global DL model.”
The paper relies on this without support.
FL inherently could address key issues about the availability of UEs and edge nodes and the unbalanced and non-IID data.
“FL inherently could address key issues about the availability of UEs and edge nodes and the unbalanced and non-IID data.”
The paper relies on this without support.
In FL, additional computation could be used to decrease the number of communication rounds to train a model.
“In FL, additional computation could be used to decrease the number of communication rounds to train a model.”
The paper relies on this without support.
An effective way to add computation is increasing computation per UE, i.e., adding more local SGD updates per round.
“An effective way to add computation is increasing computation per UE, i.e., adding more local SGD updates per round.”
The paper relies on this without support.
Learning takes long time of training as well as inferring according to the required accuracy level.
“Learning takes long time of training as well as inferring according to the required accuracy level.”
The paper relies on this without support.
In practice, massive training data are actually unable to be uploaded without any delay.
“In practice, massive training data are actually unable to be uploaded without any delay.”
The paper relies on this without support.
In the framework of FL, every client only needs to upload the update of its model.
“In the framework of FL, every client only needs to upload the update of its model.”
The paper relies on this without support.
In 5G, URLLC scenarios strongly desire very small delays and high reliability of the mobile system.
“In 5G, the defined Ultra-Reliable Low-Latency Communications (URLLC) scenarios strongly desire very small delays and high reliability of the mobile system.”
The paper relies on this without support.
General deep learning-based optimization and prediction schemes take quite long running time of recursions for converging to the results, which is inappropriate for mobile edge systems.
“However, general deep learning-based optimization and prediction schemes take quite long running time of recursions for converging to the results, which is inappropriate for mobile edge systems; particularly the system-level edge computing tasks desiring rapid responses in the scale of millisecond.”
The authors state this as an assumption.
The experiment assumes an ideal wireless environment.
“Nonetheless, this experiment assumes an ideal wireless environment.”
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?
In the citing paper
“Again, due to FL’s properties, imbalanced and non-IID information can be handled appropriately, ensuring that the AI system’s performance is not limited and that a large device in multiple contexts may be trained with their local data and local model without difficulty [211].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [211]
In the citing paper
“In [9] an adaptive control scheme was proposed to adapt the global FL aggregation frequency.”
From Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism (2020) · cites this paper as [9]
In the citing paper
“This adaptive control scheme offers a desirable tradeoff between global model aggregation and local model update to minimize the loss function with resource budget constraint.”
From Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism (2020) · cites this paper as [9]
Citations
Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · cited 4×
“By taking advantage of Deep Q-Learning and its improved version, e.g., Double DQN [12], the control policy Φ = (Φc(Υ),Φe(Υ)) could be trained and achieves increasing the utility of UEs for the long-term performance optimization.”
BackgroundInfluentialReinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · cited 14×
“Nonetheless, the privacy and security is not our focus in this work, more information about these issues could be found in references of [11] A. Integration of Federated Learning within Edge for In-Edge AI Between AI in cloud and AI in UEs, there is an “edge”.”
BackgroundMethodInfluentialReinforcement Learning: An Introduction
Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,825
A Survey on Mobile Edge Computing: The Communication Perspective
Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2745201 · Cited by 5,607 · cited 2×
“Several pioneer works have been proposed and realize quite good results in their assuming settings based on convex optimization, game theory and so on [3] [4] .”
BackgroundMethodDeep Reinforcement Learning with Double Q-Learning
Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10295 · Cited by 3,318 · cited 13×
“However, once the model merging of FL has been processed several times, the performance of DDQN with FL becomes near to that of centralized DDQN.”
MethodInfluentialTask Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network
Chen, M., & Hao, Y. (2018) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2018.2815360 · Cited by 1,085 · cited 1×
“Several pioneer works have been proposed and realize quite good results in their assuming settings based on convex optimization, game theory and so on [3] [4] .”
BackgroundSelf-citationCache in the air: exploiting content caching and delivery techniques for 5G systems
Wang, X., Chen, M., Taleb, T., Ksentini, A., & Leung, V. (2014) · IEEE Communications Magazine · doi:10.1109/mcom.2014.6736753 · Cited by 1,063 · cited 1×
“, base stations equipped with computation/storage capability, could deal with the computation and content requests of UEs, and consequently this scheme improves the Quality-of-Service (QoS) of Mobile Network Operators (MNOs) and the Quality-ofExperience (QoE) of UEs and relieves the load of backbone networks, and pres…”
BackgroundSelf-citationDeep Learning for Intelligent Wireless Networks: A Comprehensive Survey
Mao, Q., Hu, F., & Hao, Q. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2846401 · Cited by 789 · cited 1×
“, a typical 5G node is expected to have 2000 or more configurable parameters, a recent new trend is to optimize wireless communication by Artificial Intelligence (AI) techniques [4] [5], include but not limited to the application of AI for Physical Layer (PHY), Data Link Layer, and traffic control [6].”
MethodWhen Mobile Blockchain Meets Edge Computing
Xiong, Z., Zhang, Y., Niyato, D. T., Wang, P., & Han, Z. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1701095 · Cited by 570
Research works printed but not linked: 1
- 2014, Mobile Edge Computing – Introductory Technical White Paper
Cited by
Sorted by citations
Items 801–900 of 1,000
Batool, I., Fouda, M. M., & Fadlullah, Z. M. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3650877 · Cited by 4
FL-LLM: Leveraging large language models for efficient device selection in federated learning
Lu, J., Li, Q., & Li, H. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112107 · Cited by 2
Reinforcement learning for caching with space-time popularity dynamics
Sadeghi, A., Giannakis, G. B., Wang, G., & Sheikholeslami, F. (2021) · Institution of Engineering and Technology eBooks · doi:10.1049/pbte096e_ch19 · Cited by 2
Hmitti, Z. A., Ben-Ammar, H., Soyak, E. G., Kardjadja, Y., Malektaji, S., Ali, S. O., Rayani, M., Saqib, M., Taghizadeh, S., Ajib, W., et al. (2022) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn54977.2022.9868940 · Cited by 2
“Though federated learning tried to solve this problem to some level [14], the learning process is still negatively affected due to various end devices providing heterogeneous forms of data.”
BackgroundDFHelper: Help clients to participate in federated learning tasks
Wu, Z., Gao, J., Zhang, J., Li, Y., Li, Q., Guan, Z., & Zhong, C. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04081-3 · Cited by 2
“For example, [39] intend to optimize the compu-”
BackgroundShakeer, S. M., & Babu, M. R. (2023) · Recent Patents on Engineering · doi:10.2174/1872212117666230112110257 · Cited by 2
WFLTree: A Spanning Tree Construction for Federated Learning in Wireless Networks
Li, S., Zheng, Y., & Zou, Y. (2023) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126623502201 · Cited by 2
Enabling On-Demand Crowdsourced Federated Learning Over IoT
Tahir, M., & Ali, M. I. (2023) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec59375.2023.10306078 · Cited by 2
“Advancements in FL provide a great opportunity to train a collaborative ML model while keeping the data at the device level [2].”
BackgroundFataliyev, T. K., Bayramov, H., & Mikayilova, R. (2023) · International Conference on Problems of Cybernetics and Informatics (PCI) · doi:10.1109/pci60110.2023.10326016 · Cited by 2
“The application of mobile Edge-AI methods to solve this type of data problem, especially in 5G networks, allows for quickly and comprehensively analyzing data to make better decisions [20].”
MethodScience 4.0 as a Model of Scientific Activity in an Innovative Environment of Industry 4.0
Mehdiyev, S., & Fataliyev, T. K. (2024) · International Journal of Cyber Research and Education · doi:10.4018/ijcre.339191 · Cited by 2
Proactive Caching in the Edge-Cloud Continuum with Federated Learning
Zyrianoff, I., Montecchiari, L., Trotta, A., Gigli, L., Kamienski, C., & Felice, M. D. (2024) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc51664.2024.10454774 · Cited by 2
“Another noteworthy contribution was made by Wang et al [9], as a framework to combine Deep Reinforcement Learning and FL to decide which resources to cache.”
Ren, X., Wang, Y., Zhang, J., & Han, Z. (2023) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads60453.2023.00158 · Cited by 2
“While cloud servers offer substantial computational resources, communication with the cloud server is often slow and unpredictable due to network bandwidth constraints and transmission latency[4].”
BackgroundA Fault-Tolerant Mobility-Aware Caching Method in Edge Computing
Ma, Y., Zhao, H., Guo, K., Xia, Y., Wang, X., Niu, X., Zhu, D., & Dong, Y. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.048759 · Cited by 2
Self-citationBlockchain for Edge Computing in Smart Environments: Use Cases, Issues, and Challenges
Prabadevi, B., Deepa, N., Rajan, S. S., & Srivastava, G. (2024) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126624300095 · Cited by 3
Enhancing IoT Security Through Deep Learning: A Comprehensive Study
Mishra, K., & Dutta, T. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-2839-8_22 · Cited by 2
In-Orbit Computation and Security Authentication for Satellite-Ground Twin Networks
Gong, Y., Liu, X., Yao, H., Cheng, X., Nallanathan, A., & Karagiannidis, G. K. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3467156 · Cited by 2
“Wang et al. [7] designed a native edge intelligence framework to reduce the average sys-tem overhead while optimizing communication, computation and caching resources.”
BackgroundFinding MIDDLE Ground: Scalable and Secure Distributed Learning
Bornstein, M., Nazir, N., Drgoňa, J., Kundu, S., & Adetola, V. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679587 · Cited by 2
Effective DDoS Security Scheme for Mobile Cloud Computing Systems
Ishak, C., & Saied, Y. B. (2024) · IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN) · doi:10.23919/pemwn62766.2024.10737582 · Cited by 2
Decentralization and Federated Approach for Personal Data Protection and Privacy Control
Singh, S., Verma, S. B., Gupta, B. K., & Agrawal, A. (2024) · Journal of Information assurance and security · doi:10.2478/ias-2024-0014 · Cited by 2
“By moving away from centralized models, these approaches prioritize security, privacy, and data autonomy, empowering individuals and organizations to have greater control over their data while fostering collaborative and privacy-preserving environments [19].”
Guan, B., Ren, Y., & Zhang, Y. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.116396 · Cited by 2
Energy-Efficient Resource Allocation for Satellite-UAV-Ground Integrated Compute First Networking
Zhang, Y., Hou, G., Liu, J., Feng, J., Chen, P., Chen, C., & Pei, Q. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3610868 · Cited by 2
“In recent years, the advancement of satellite IoT technology has emerged as a core research direction for constructing air-space cooperative networks due to its wide coverage and independence from ground conditions [16].”
Araujo, S. D. C. S., Goh, M. K. O., Deshpande, U. U., Deshpande, S., Avalappa, M. G., Amasi, Y., Patil, S., Bhat, S., & Karigoudar, S. (2025) · Frontiers in Robotics and AI · doi:10.3389/frobt.2025.1680285 · Cited by 4
“…angle distortions, and background complexities, and hence demonstrating its robust inference (Krogh, 2008; Albawi et al., 2017), complying with real-time object handling, collision avoidance, and warehouse navigation (Wang et al., 2019; Cahyo and Utaminingrum, 2022) operational requirements.”
Collaborative learning with taboos for machine learning methods in big data problems
Połap, D., & Woźniak, M. (2020) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308296 · Cited by 2
“For this purpose very important is communication way with the main server to upload/download calculated coefficients (see [10]).”
BackgroundFederated Learning at Mobile Edge Networks: A Tutorial
Lim, W. Y. B., Ng, J. S., Xiong, Z., Niyato, D. T., & Miao, C. (2022) · Wireless networks · doi:10.1007/978-3-031-07838-5_1 · Cited by 4
MeFILL: A Multi-edged Framework for Intelligent and Low Latency Mobile IoT Services
Gu, R., Yu, L., & Zhang, J. (2020) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc45663.2020.9120786 · Cited by 2
“The In-Edge AI [13] was proposed to utilize the collaboration between end devices and edge servers to exchange the parameters for better results.”
MethodOptimization and Learning for Data Offloading and Resource Management in Mobile Edge Computing
Yang, Y., & Gursoy, M. C. (2021) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops51409.2021.9430862 · Cited by 2
“Due to the unprecedented growth in mobile data traffic and the increasing computational requirements in emerging mobile applications, cellular networks and data servers are experiencing high communication and computational loads [1].”
BackgroundA cache placement algorithm based on comprehensive utility in big data multi-access edge computing
Liu, Y., Huang, W. K., Han, L., & Wang, L. (2021) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2021.11.002 · Cited by 2
“Wang X et al. [14] proposed an integration of the deep reinforcement learning technology and federated learning framework with mobile edge systems to optimize mobile edge computing, caching, and communication.”
BackgroundHybridEI: Smartly Face Detection System in Resource Constrained Edge Environment
Najafi, F., Wang, P., & Nyabuga, D. O. (2021) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc52481.2021.9702180 · Cited by 2
“Furthermore, some studies have been performed, such as [2] on directly running DL methods such as convolutional neural networks (CNNs) on resource-limited EC gadgets, resulting in the evolution of EI, also termed as mobile intelligence (MI).”
BackgroundMeira, N. F. D. C., Silva, M. C., Vieira, C. B., Souza, A. C. C., & Oliveira, R. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-08965-7_8 · Cited by 2
A Framework for Named Networking on Edge AI
Flores, F. K., & Peradilla, M. S. (2022) · International Conference on Ubiquitous and Future Networks · doi:10.1109/icufn55119.2022.9829714 · Cited by 2
“NFN is an extension NDN, such that it does not only support naming of data and objects, but also function definitions and application to data as well [6].”
BackgroundMEC‐Based Cooperative Multimedia Caching Mechanism for the Internet of Vehicles
Han, L., Li, S., Ao, C., Liu, Y., Liu, G., Zhang, Y., & Zhao, J. X. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/8777890 · Cited by 2
“The relationship between the content request probability and the content popularity level can be expressed as [27]”
BackgroundModeling and Analysis of Intermittent Federated Learning Over Cellular-Connected UAV Networks
Liu, C., Liang, D.-C., Gau, R.-H., & Wei, L. (2022) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2022-spring54318.2022.9860913 · Cited by 2
Cai, Y., Li, S., Xia, Z., & Tan, Y. (2022) · IEEE International Conference on Control Science and Systems Engineering (ICCSSE) · doi:10.1109/iccsse55346.2022.10079164 · Cited by 2
“When the rounds of training are limited, scheduling the maximum number of the users or adapting the number of local compute steps to the global gradient is both an efficient method [2], [3].”
MethodAI/ML for Computation Offloading
Taheri, J., Dustdar, S., Zomaya, A. Y., & Deng, S. (2022) · Springer eBooks · doi:10.1007/978-3-031-22155-2_4 · Cited by 2
Narmadha, C., Muthuselvi, R., Somasundari, P., Sivagurunathan, G., Malini, K. V., & Kannan, S. (2023) · International Journal of Electronics and Communication Engineering · doi:10.14445/23488549/ijece-v10i4p103 · Cited by 2
Perancangan Federated Learning Berbasis Homomorphic Encryption untuk Perangkat Internet of Things
Saputra, Y. M., Alfian, G., & Octava, M. Q. H. (2023) · Journal of Internet and Software Engineering · doi:10.22146/jise.v4i1.6378 · Cited by 2
Fostering Trustworthiness of Federated Learning Ecosystem through Realistic Scenarios
Psaltis, A., Zafeirouli, K., Leškovský, P., Bourou, S., Vásquez-Correa, J. C., Pablos, A. G., Sanchez, S. C., Dimou, A., Patrikakis, C. Z., & Daras, P. (2023) · Information · doi:10.3390/info14060342 · Cited by 2
“Wang, X. et al. [40] discussed the convergence behavior of FL based on gradient-descent in non-IID data background and provided different convergence theorems for FedAvg in non-IID situations.”
BackgroundFedmpo : federated optimization based on multidimensional especially 3-dimensional proximal operator
Jiang, F., Yang, X., Li, Y., & Li, L. (2023) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-01956-w · Cited by 2
Identifying New Vulnerabilities Embedded in Consumer Internet of Things (IoT) Devices
Mingo, H. C., Lawson, M. N., & Williamson, A. C. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-3226-9.ch011 · Cited by 2
Optimizing Cache Memory Usage Methods for Chat LLM-models in PaaS Installations
Rovnyagin, M. M., Sinelnikov, D. M., Eroshev, A. A., Rovnyagina, T. A., & Tikhomirov, A. V. (2024) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/elcon61730.2024.10468250 · Cited by 2
“In [7], the authors study the problem of training large language models on edge devices and consider several ways to train the model on a distributed system.”
BackgroundSURVEY ON TECHNICAL ADVANCEMENTS AND RENOVATIONS IN FEDERATED LEARNING
Subramanian, B., Tulshain, S., MananModi, Dalei, A., & Sumathi, D. (2024) · Malaysian Journal of Computer Science · doi:10.22452/mjcs.vol37no1.2 · Cited by 2
Heterogeneity-Aware Federated Learning with Adaptive Local Epoch Size in Edge Computing
Yao, W., Liu, T., Cui, Y., & Zhu, Y. (2023) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn60784.2023.00036 · Cited by 2
“To this end, Edge Intelligence [1], [2] has been proposed to push AI from central cloud to network edge, leveraging the computing power of devices to perform model training.”
MethodAI at the Edge: Cloud-Edge Synergy
Bellala, K. R. (2025) · doi:10.38124/ijisrt/25may967 · Cited by 2
Efficient Semi-Online Algorithms for Multiple Objects in Computational Offloading
Song, S., Fang, Z., Chu, S., & Bai, M. (2019) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001420580148 · Cited by 2
Traffic Offloading for Content Distribution Assisted With Device-to-Device Communications
Song, W., & Xing, H. (2020) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2020.2992305 · Cited by 2
A New Caching Algorithm for Boosting Edge Computing Performance
Zhang, Z., & Hao, W. (2020) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon51285.2020.9298173 · Cited by 2
Resource Constrained Neural Networks for Direction-of-Arrival Estimation in Micro-controllers
Sahoo, P., Rajoria, R., Chandhok, S., Darak, S. J., Pau, D. P., & Dabral, H.-D. (2021) · International Conference on AI-ML Systems (AIMLSystems) · doi:10.1145/3486001.3486230 · Cited by 2
“0, and distributed networks to reality [12, 42].”
Fair Federated Learning for Heterogeneous Face Data.Preprint
Kanaparthy, S., Padala, M., Damle, S., & Gujar, S. (2021) · arXiv (Cornell University) · Cited by 2
Towards dependable 6G networksPreprint
Rodriguez, F., Ahmad, I., Huusko, J., & Seppänen, K. (2022) · doi:10.36227/techrxiv.20237358.v1 · Cited by 2
Deep Reinforcement Learning and Its Applications
Hoang, D. T., Huynh, N. V., Nguyen, D. N., Hossain, E., & Niyato, D. T. (2023) · Wiley eBooks · doi:10.1002/9781119873747.ch1 · Cited by 2
Kumar, A., Kumar, V., Mahto, M. K., & Bhadauria, A. P. S. (2026) · doi:10.1002/9781394305698.ch13 · Cited by 2
Guidelines for benchmarking non-ideal analog memristive crossbars for neural networks
James, A. P. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821184-7.00023-2 · Cited by 1
AFL-CS: Asynchronous Federated Learning with Cosine Similarity-based Penalty Term and Aggregation
Yan, B., Jiang, X., Chen, Y., Gao, C., & Liu, X. (2023) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads60453.2023.00016 · Cited by 2
“Furthermore, FL has achieved numerous successful applications in other domains such as finance [26], [27], IoT [28]– [31], etc.”
BackgroundHu, Y., Lou, Z., Zhu, Z., & Xu, T. (2023) · International Conference on Mechanical and Electronics Engineering · doi:10.1109/icmee59781.2023.10525541 · Cited by 1
“For this issue, some researches only take the large-scale fading effect into consideration [5]–[7].”
BackgroundMethodApplications of big data analytics in IoT
Tariq, A., Ali, L., Sallabi, F., Alnajjar, F. S. K., & Jassmi, H. A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-21640-4.00007-7 · Cited by 1
A systematic review of the foundations, applications, and challenges of federated learning
Fateh, H., Rezvani, M., & Tahanian, E. (2024) · Archives de l'Institut d'Hessarek. · doi:10.61186/jsdp.21.3.23 · Cited by 1
Delay-Optimized Task Scheduling Based on Deep Reinforcement Learning in Computing Power Networks
Tang, Q., Xu, D., Xie, R., Feng, L., Chen, T., & Huang, T. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-2468-3_39 · Cited by 1
Collaborative Inference in RIS-Assisted MEC Networks Under Computing Backlog Constraints
Yang, Y., & Gursoy, M. C. (2025) · IEEE Transactions on Communications · doi:10.1109/tcomm.2025.3564727 · Cited by 1
Diversified caching algorithm with cooperation between edge servers
Sang, Y.-X., Guo, Y., Wang, B., & Song, Y. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2824 · Cited by 1
“Literature ( Wang et al., 2019 ) proposed the “ In-Edge AI ” framework, which integrates deep reinforcement learning and federated learning into mobile edge systems to optimize computing, caching, and communication, demonstrating near-optimal performance with low learning costs and strong…”
Introducing a Quality-Driven Approach for Federated Learning
Usman, M., Bernardi, M. L., & Cimitile, M. (2025) · Sensors · doi:10.3390/s25103083 · Cited by 1
“FL has demonstrated promising results in various applications [13], yet it is not without challenges.”
Lightweight cloud service composition and orchestration for edge intelligence
Xiao, L., Shan, H., Zhu, J., Mao, R., & Pan, S. (2025) · Intelligent Decision Technologies · doi:10.1177/18724981241301911 · Cited by 1
“7 By leveraging the computing and storage resources of edge devices, edge intelligence aims to process and analyze data closer to its source, reducing latency and improving privacy.”
Private linear equation solving: An application to federated learning and extreme learning machines
Heinlein, D., Akusok, A., Björk, K.-M., & Espinosa-Leal, L. (2025) · Journal of Computational Science · doi:10.1016/j.jocs.2025.102693 · Cited by 1
Role of IoT in Disaster Monitoring and Response: A Comprehensive Review
Talal, M., Ajmal, N., Shahrukh, Rehman, A., & Mamoon, M. (2025) · VFAST Transactions on Software Engineering · doi:10.21015/vtse.v13i3.2193 · Cited by 2
NeuroCivitas: A Federated Deep Learning Model for Adaptive Urban Intelligence in 6G Cognitive Cities
Aloshban, N., & Alharbi, A. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.067523 · Cited by 1
“Most of the current approaches either sacrifice accuracy for privacy or achieve privacy with a degraded model performance, rendering them impractical for deployment in smart cities [16].”
InfluentialVanishing Privacy: Fast Gradient Leakage Threat to Federated Learning
Jiang, Y., Liu, X., & Yan, Q. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228471 · Cited by 1
A Self-Improving Robotic System for Medical Waste Management Using Federated Continual Learning
Aktaruzzaman, M., Islam, M. S., Bappy, M. A. H., & RAFID, M. M. (2025) · IEEE International Conference on Computing, Applications and Systems (COMPAS) · doi:10.1109/compas67506.2025.11381841 · Cited by 1
“Federated Learning (FL) offers a powerful paradigm for decentralized machine learning, enabling collaborative model building while keeping raw data on-premises [13].”
Machine Learning-Based Caching and Tiering in Modern Data Storage Systems: A Survey
Savva, G., Kakoulli, E., & Herodotou, H. (2026) · ACM Computing Surveys · doi:10.1145/3803857 · Cited by 1
“For instance, Wang et al. [88] propose an ML-based framework that jointly optimizes mobile edge computing, caching, and communication.”
Defending federated learning against adversarial attacks: A systematic literature review
Babar, F. F., Khan, S., & Parkinson, S. (2026) · Cyber Security and Applications · doi:10.1016/j.csa.2026.100127 · Cited by 1
Edge AI in the social internet of things: a systematic review of trends and research needs
Riehle, D. M., von Straussenburg, A. F. A., & Blazevic, M. (2026) · Management Review Quarterly · doi:10.1007/s11301-026-00588-y · Cited by 1
Adaptive Computation Offloading Decision Optimization in MEC-Assisted FL
Iv, T., Ros, S., Sam, S., & Kim, S. (2026) · Journal of Network and Systems Management · doi:10.1007/s10922-026-10086-x · Cited by 1
“Providing [11], the authors combined the idea of edge computing and FL to design a framework with DRL approach implementation, namely In-Edge AI, to utilize MEC resources and FL data privacy to enable collaborative learning and enhance mobile user experience.”
Deng, J., Liu, J., Liu, Y., Hu, C., Li, Y., Tao, Y., Yang, Y., Wang, H., & Yan, J. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3706440 · Cited by 1
Edge AI on Constrained Devices: A Survey of Deployment, Optimization, and Lifecycle Management
Passarotto, M., Abdesslem, F. B., Eriksson, J., & Voigt, T. (2026) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3827611 · Cited by 1
“Despite these advances, signiicant challenges remain: frequent synchronization rounds can impose high communication overhead on wireless links [255], heterogeneous data and hardware often slow convergence, and intermittent participation reduces eiciency.”
Generative AI for mobile edge computing
Zhao, T., Chen, X., Sun, M., & Zhou, X. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-341497-8.00023-7 · Cited by 1
Self-citationEdge artificial intelligence: Past advances, current paradigms, and emerging horizons
Ali, M. A., Dornaika, F., & Charafeddine, J. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115635 · Cited by 1
Thakur, V., More, V. N., & Bhatt, A. (2026) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2026.103940 · Cited by 1
FAULT TOLERATING MECHANISM IN DISTRIBUTED COMPUTING ENVIRONMENT
Gour, L., & Waoo, A. A. (2020) · International Journal of Engineering Applied Sciences and Technology · doi:10.33564/ijeast.2020.v05i04.096 · Cited by 1
An improved reinforcement learning approach to solve flow job scheduling problems
Hu, D., Jiang, X., & Wang, J. (2020) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-smartcity-dss50907.2020.00067 · Cited by 1
“With the development of current deep learning technology(DRL), Xiaofei Wang,et al. [9] proposed to combine the DRL technology and changeable frame with the mobile edge system to make the mobile edge system more intelligent than the traditional optimization methods.”
MethodEdge Caching Strategy Based on User Preference and Game Theory
Jia, X. (2021) · Academic Journal of Computing & Information Science · doi:10.25236/ajcis.2021.040412 · Cited by 1
RF Fingerprint Recognition in Resource-Constrained Edge Computing
Liu, D., Wang, H., & Wang, M. (2021) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip53232.2021.9674057 · Cited by 1
“Wang et al [5] combined deep reinforcement learning techniques and distributed machine learning frameworks with mobile edge computing to design an "In-Edge-AI" framework that achieves dynamic optimization and enhancement of machine learning algorithms while reducing unnecessary system communication load. proposed a me…”
BackgroundFederated Learning for 6G Edge Intelligence: Concepts, Challenges and Solutions
Wang, H., Hu, J., Xing, C., & Zhang, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-96033-9_9 · Cited by 1
Resources in IoT Through Bctechnology
Goyal, M. K., & Sharma, K. (2023) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite57410.2023.10182809 · Cited by 1
“By relocating computation, algorithms, and services from administrative nodes to the network edges rapid, and position-based services can be delivered [12-14].”
MethodNetwork Traffic Analysis as a Strategy for Cache Management
Haraty, R. A., & Rahal, N. (2023) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/1401106 · Cited by 1
“According to [17-18], both qualities of service and experience shall be improved with this approach.”
BackgroundPower-Delay Tradeoff in Vehicle-to-Edge Networks Using Constrained Reinforcement Learning
Hlophe, M. C., Awoyemi, B. S., & Maharaj, B. T. (2023) · IEEE AFRICON · doi:10.1109/africon55910.2023.10293618 · Cited by 1
“All these are aimed at meeting the practical requirements of applications such as autonomous vehicles and other compute-intensive applications [1], which imposes a huge amount of traffic on the already congested network backbone [2].”
BackgroundA Distributed Framework for the Ocean IoT Network
Ning, J., Wang, J., Feng, P., & Yang, T. (2023) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc56721.2023.10294049 · Cited by 1
“This data growth results in significant storage space consumption and communication bandwidth demands, leading to increased latency and reduced service quality [1].”
BackgroundTowards Energy-efficient Resource Allocation for Federated Learning in Mobile Edge Computing
Ma, W., Zhao, Y., Sun, W. Y., Liu, Y., Guo, B., & Niyato, D. T. (2023) · International Conference on Artificial Intelligence of Things and Systems (AIoTSys) · doi:10.1109/aiotsys58602.2023.00060 · Cited by 1
“Regrettably, the widespread deployment of AI applications on edge devices is deemed impracticable due to the obstacles presented by data silos and the limited availability of MEC resources [3].”
BackgroundAn Edge Internet of Things Framework for Machine Learning-Based Skin Cancer Detection Models
Kanjula, K. R., Datla, A. R., Chen, T., & Kabir, M. F. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00327 · Cited by 1
“This paradigm has led to a growth in intriguing IoT applications, such as healthcare monitoring systems [5], edge-deep reinforcement learning for wireless mobile networks [6], and improving computational speeds on mobile networks through edge-deep neural networks [7].”
BackgroundOptimizing Model Dissemination for Hierarchical Clustering Learning in Edge Computing
Zhang, L., Feng, G., Qin, Z., & Li, X. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3401753 · Cited by 1
HASFL: Harnessing Heterogeneous Models Across Diverse Devices for Enhanced Federated Learning
Hao, J.-S., Dong, F., Cen, B.-H., Fu, S.-C., Zhou, R., & Ding, D. (2024) · International Conference on Parallel Processing · doi:10.1145/3673038.3673133 · Cited by 2
“Edge computing [19] aims to process data closer to the devices that generate it to reduce response delays between devices and the server.”
Optimized task offloading for federated learning based on β-skeleton graph in edge computing
Fallah, M., & Salehpour, P. (2024) · Telecommunications Systems · doi:10.1007/s11235-024-01216-4 · Cited by 1
“The importance of these concepts lies in their capacity to bridge the gap between the capabilities of the cloud and the requirements of devices at network edges, ultimately enhancing content delivery and strengthening mobile services [1].”
Artificial Intelligence Workload Allocation Method for Vehicular Edge Computing
Rafea, S. A., & Jasim, A. D. (2024) · Journal of Information Systems Engineering & Management · doi:10.55267/iadt.07.15495 · Cited by 1
TS-FedNBS: Federated Edge Computing with Enhanced Robustness
Basharat, A., Wang, R., & Xu, P. (2024) · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc62439.2024.00015 · Cited by 1
“…adopted in edge computing applications and various robust aggregation methods have been developed to enhance resilience against adversarial attacks [11], [12], the critical role of robust aggregation at the central server has not been sufficiently emphasized or studied. for node i = 1 , . . . , E…”
eFLEET: a framework in federated learning for enhanced electric transportation
de Gauna, D. E. R., Ruiz‐Iniesta, A., & Crespo, L. E. S. (2025) · International Journal of Web Information Systems · doi:10.1108/ijwis-09-2024-0281 · Cited by 1
CADAEC: Content-Aware Deployment of AI Workloads in Edge-Cloud Ecosystem
Saha, R. K., Mittal, S., Singhal, R., & Nambiar, M. (2025) · ACM/SPEC International Conference on Performance Engineering (ICPE) · doi:10.1145/3676151.3719355 · Cited by 1
“Alongside these allocation techniques, distillation-based methods [19] and pruning strategies, such as those in [7] and [8], have also been explored to enhance edge-based deployments.”
Kumar, G. K., & Thirumaran, M. (2025) · International Conference Trends Material Science and Inventive Materials · doi:10.1109/ictmim65579.2025.10988259 · Cited by 1
“However, the hyperparameters of deep learning models have been optimized using the algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) [5].”
Edge AI: Revolutionizing IoT Data Processing
Darla, M. S. K. (2025) · Journal of Computer Science and Technology Studies · doi:10.32996/jcsts.2025.7.7.26 · Cited by 1
Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-The-Air Computation
Ma, Q., Zhou, J., Hou, X., Liu, J., Xu, H., Miao, J., & Jia, Q. (2025) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps64566.2025.00051 · Cited by 3
“Recently, artificial intelligence (AI) algorithms have deployed from the centralized cloud to the distributed network edge, which is known as edge AI [2], enabling efficient data processing locally.”
A sparse and invisible targeted backdoor attack in federated learning
Zhang, Q., Yu, M., Wang, R., Li, Y., Yuan, J., & Tan, Y. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00146-8 · Cited by 1
Detect, Adapt, Overcome: Mitigating Concept Drift in Federated Learning
Rahman, I., Hemadasa, N., Kaaser, D., Murena, P.-A., & Schulte, S. (2025) · International Conference on Federated Learning Technologies and Applications (FLTA) · doi:10.1109/flta67013.2025.11336319 · Cited by 1
“Edge computing addresses this trend by processing data locally on devices at the periphery of a network [20].”
Zhang, Q., Ren, X., Cao, Y., Zhang, H., Wang, X., & Leung, V. C. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60259-8_9 · Cited by 1
Self-citationIterative Optimization for Edge Federated Learning
Chai, S., Gu, M., Zhou, J., Zhang, W., & Zeng, X. (2020) · Communications in computer and information science · doi:10.1007/978-981-33-4336-8_2 · Cited by 1
Metadata
- Authors
- Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., & Chen, M.
- Affiliations
- Tianjin University (CN)
- Huawei Technologies (China) (CN)
- Arizona State University (US)
- Huazhong University of Science and Technology (CN)
- Venue
- IEEE Network
- Published
- 2019-09
- Volume
- 33
- Issue
- 5
- Pages
- 156-165
- ISSN
- 0890-8044 (ISSN-L)
- 1558-156X
- OpenAlex
- W2962804345
- Semantic Scholar
- 6f89a632ceb8fcb81eac3d7b52e937099659cc6a
- Cited by
- 1,030
- References
- 17
- Citations by year
- 2026: 44 · 2025: 95 · 2024: 127 · 2023: 165 · 2022: 215 · 2021: 196 · 2020: 160 · 2019: 26
- FWCI
- 81.72
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Network (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Keywords
- mobile edge computing, federated learning, deep reinforcement learning, edge caching, content distribution, communication burden reduction, edge nodes, device collaboration, dynamic optimization
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