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 101–200 of 1,000
Robust Federated Learning With Noisy Communication
Ang, F., Chen, L., Zhao, N., Chen, Y., Wang, W., & Yu, F. R. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.2979149 · Cited by 144
Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks
Feng, C., Yang, H. H., Hu, D., Zhao, Z., Quek, T. Q. S., & Min, G. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3166386 · Cited by 157
“It provides great convenience for parallel processing and can reduce the costs of message exchanging significantly [2], [3].”
LSFL: A Lightweight and Secure Federated Learning Scheme for Edge Computing
Zhang, Z., Wu, L., Ma, C., Li, J., Wang, J., Wang, Q., & Yu, S. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3221899 · Cited by 143
“For example, Wang et al. [44], [45] proposed algorithms to solve the problem of insufficient resources when edge nodes participate in FL.”
BackgroundMobility Support for Millimeter Wave Communications: Opportunities and Challenges
Li, J., Niu, Y., Wu, H., Ai, B., Chen, S., Feng, Z., Zhong, Z. D., & Wang, N. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3176802 · Cited by 136
“A recent new trend is to optimize mmWave systems with AI techniques [255].”
Method6G-enabled Edge Intelligence for Ultra -Reliable Low Latency Applications: Vision and Mission
Gupta, R., Reebadiya, D., & Tanwar, S. (2021) · Computer Standards & Interfaces · doi:10.1016/j.csi.2021.103521 · Cited by 137
FedSteg: A Federated Transfer Learning Framework for Secure Image Steganalysis
Yang, H., He, H. X., Zhang, W., & Cao, X. (2020) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.2996612 · Cited by 134
AI-Based Mobile Edge Computing for IoT: Applications, Challenges, and Future Scope
Singh, A. P., Satapathy, S. C., Roy, A., & Gutub, A. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-06348-2 · Cited by 142
“A more intelligent AI-based edge system has been given in [125].”
Stochastic Client Selection for Federated Learning With Volatile Clients
Huang, T., Lin, W., Shen, L., Li, K., & Zomaya, A. Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3172113 · Cited by 141
“• Multi-level model aggregation: Lately, mobile edge computing (MEC)-enabled FL [18] has received notable research interest.”
Background6G service-oriented space-air-ground integrated network: A survey
Cheng, N., He, J., Yin, Z., Zhou, C., Wu, H., Lyu, F., Zhou, H., & Shen, X. (2021) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2021.12.013 · Cited by 149
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey
Wu, J., Dong, F., Leung, H., Zhu, Z., Zhou, J., & Drew, S. (2024) · ACM Computing Surveys · doi:10.1145/3659205 · Cited by 130
Edge Intelligence: Architectures, Challenges, and ApplicationsPreprint
Xu, D., Li, T., Li, Y., Su, X., Tarkoma, S., Jiang, T., Crowcroft, J., & Hui, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12172 · Cited by 204
“This new paradigm of intelligence is called edge intelligence [18], ar X iv :2 00 3.”
BackgroundChallenges and future directions of secure federated learning: a survey
Zhang, K., Song, X., Zhang, C., & Yu, S. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-021-0598-z · Cited by 127
Nguyen, T. D., Nguyen, T., Nguyen, P. L., Pham, H. H., Doan, K. D., & Wong, K.-S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107166 · Cited by 130
FogFL: Fog-Assisted Federated Learning for Resource-Constrained IoT Devices
Saha, R., Misra, S., & Deb, P. K. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3046509 · Cited by 125
“Wang et al. [13] integrated deep reinforcement learning and FL techniques with mobile-edge computing (MEC) and proposed an edge-AI framework to optimize the MEC, caching, and communication.”
MethodHybrid Blockchain-Based Resource Trading System for Federated Learning in Edge Computing
Fan, S., Zhang, H., Zeng, Y., & Cai, W. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3028101 · Cited by 125
PEFL: Deep Privacy-Encoding-Based Federated Learning Framework for Smart Agriculture
Kumar, P., Gupta, G. P., & Tripathi, R. (2021) · IEEE Micro · doi:10.1109/mm.2021.3112476 · Cited by 124
Privacy-preserving Federated Deep Learning for Wearable IoT-based Biomedical Monitoring
Can, Y. S., & Ersoy, C. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3428152 · Cited by 123
“Wang [16] Mobile Edge Computing Edge Network Comm.”
BackgroundHe, W., Guo, S., Guo, S., Qiu, X., & Qi, F. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2981338 · Cited by 122
“Mobile-edge computing (MEC) has become a promising technology to solve such problems [3].”
BackgroundQuantized Federated Learning Under Transmission Delay and Outage Constraints
Wang, Y., Xu, Y., Shi, Q., & Chang, T. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126081 · Cited by 132
Cui, L., Su, X., Ming, Z., Chen, Z., Yang, S., Zhou, Y., & Xiao, W. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3014370 · Cited by 146
Computation Offloading for Edge-Assisted Federated Learning
Ji, Z., Chen, L., Zhao, N., Chen, Y., Wei, G., & Yu, F. R. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3098022 · Cited by 118
“Many existing works have applied federated learning in different aspects of the MEC system, such as guiding the network resource allocation [31], optimizing the network routing [32], or reducing the communication overhead of learning tasks [33].”
BackgroundÜnal, D., Hammoudeh, M. A. A., Khan, M. A., Abuarqoub, A., Epiphaniou, G., & Hamila, R. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102393 · Cited by 116
AI-Enhanced Cloud-Edge-Terminal Collaborative Network: Survey, Applications, and Future Directions
Gu, H., Zhao, L., Han, Z., Zheng, G., & Song, S. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3338153 · Cited by 124
Blockchain-Based Decentralized Authentication Modeling Scheme in Edge and IoT Environment
Ma, Z., Jialin, M., Jihui, W., & Shan, Z. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3037733 · Cited by 113
“In order to further optimize the edge system, Xiaofei Wang et al. [25] designed the “In-Edge AI” framework, proposed to integrate the deep reinforcement learning techniques and federated learning framework with the mobile edge systems.”
MethodA Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services
Katare, D., Perino, D., Nurmi, J., Warnier, M., Janssen, M., & Ding, A. Y. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3302474 · Cited by 135
“As shown in above subsections, the work to overcome communication overhead combines the use of sparsification and gradient quantization [34], [309], [168].”
BackgroundResource Allocation for Edge Computing in IoT Networks via Reinforcement Learning
Liu, X., Qin, Z., & Gao, Y. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8761385 · Cited by 112
“In [13], an In-Edge artificial intelligence has been evaluated and could achieve near-optimal performance by investigating the scenarios of edge caching and computation offloading in MEC systems.”
MethodMobile-Edge-Computing-Based Hierarchical Machine Learning Tasks Distribution for IIoT
Yang, B. F., Cao, X., Li, X., Zhang, Q., & Qian, L. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2959035 · Cited by 112
“Concurrently with this article, the ML techniques are introduced to achieve the optimal resource allocation for the computation offloading system in [34] and [35].”
BackgroundMethodPFLF: Privacy-Preserving Federated Learning Framework for Edge Computing
Zhou, H., Yang, G., Dai, H., & Liu, G. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3174394 · Cited by 116
Lee, J., Solat, F., Kim, T. Y., & Poor, H. V. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3352910 · Cited by 125
“The article in [189] provided a DRL-based FL framework over MEC servers to jointly optimize content caching and offloading management.”
MethodFL-Defender: Combating targeted attacks in federated learning
Jebreel, N. M., & Domingo-Ferrer, J. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110178 · Cited by 110
From distributed machine learning to federated learning: In the view of data privacy and security
Shen, S., Zhu, T., Wu, D., Wang, W., & Zhou, W. (2020) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6002 · Cited by 134
Privacy-preserving blockchain-enabled federated learning for B5G-Driven edge computing
Wan, Y., Qu, Y., Gao, L., & Xiang, Y. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108671 · Cited by 114
Nair, A. K., Sahoo, J., & Raj, E. D. (2023) · Computer Standards & Interfaces · doi:10.1016/j.csi.2023.103720 · Cited by 120
A survey of mobility-aware Multi-access Edge Computing: Challenges, use cases and future directions
Singh, R., Sukapuram, R., & Chakraborty, S. (2022) · Ad Hoc Networks · doi:10.1016/j.adhoc.2022.103044 · Cited by 107
6G-Enabled Network in Box for Internet of Connected Vehicles
Lv, Z., Qiao, L., & You, I. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3034817 · Cited by 103
Resource Management in Mobile Edge Computing: A Comprehensive Survey
Zhang, X., & Debroy, S. (2023) · ACM Computing Surveys · doi:10.1145/3589639 · Cited by 111
“In [96], authors compare the pros and cons of several training schemes of a DRL agent.”
BackgroundWang, H., Zhang, W., Yang, D., & Xiang, Y. (2022) · IEEE Systems Journal · doi:10.1109/jsyst.2022.3193200 · Cited by 119
“This technology provides unprecedented intelligence for the IoTs, cloud computing, and other techniques, and satisfies the demands of real-time business, data analysis, edge intelligence [85], data privacy and security [86], and other aspects.”
BackgroundA Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and Trends
Neto, H. N. C., Hribar, J., Dusparić, I., Mattos, D. M. F., & Fernandes, N. C. (2023) · IEEE Access · doi:10.1109/access.2023.3269980 · Cited by 114
“compute offload decisions within said system [123].”
Method6G: The Intelligent Network of Everything
Pennanen, H., Hänninen, T., Tervo, O., Tölli, A., & Latva‐aho, M. (2024) · IEEE Access · doi:10.1109/access.2024.3521579 · Cited by 117
Lian, Z., Yang, Q., Wang, W., Zeng, Q., Alazab, M., Zhao, H., & Su, C. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3175945 · Cited by 100
“The authors [23]–[25] exploited to combine edge computing with federated learning in some fields (e.g., Internet of Things, social network), which results in high performance on convergence and efficient training.”
BackgroundEdge Intelligence: Concepts, Architectures, Applications, and Future Directions
Mendez, J., Bierzynski, K., Cuéllar, M. P., & Morales, D. P. (2022) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3486674 · Cited by 113
“ML algorithms have been already adapted and deployed at the network edge using the classic AI concept of Intelligent Agent [180].”
MethodOptimal Resource Allocation and Task Segmentation in IoT Enabled Mobile Edge Cloud
Mahmood, A., Hong, Y., Ehsan, M. K., & Mumtaz, S. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3121146 · Cited by 98
Toward Communication-Efficient Federated Learning in the Internet of Things With Edge Computing
Sun, H., Li, S., Yu, F. R., Qi, Q., Wang, J., & Liao, J. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2994596 · Cited by 95
“It has several benefits of data privacy [15], [16], and utilizes the potential computation resources of mobile devices.”
Security and Privacy Threats to Federated Learning: Issues, Methods, and Challenges
Zhang, J., Zhu, H., Wang, F., Zhao, J., Xu, Q., & Li, H. (2022) · Security and Communication Networks · doi:10.1155/2022/2886795 · Cited by 105
“'e implementation of FL in mobile edge networks accelerates content delivery and improves mobile service quality by reducing unnecessary system communication load [10].”
BackgroundThree-Stage Stackelberg Game Enabled Clustered Federated Learning in Heterogeneous UAV Swarms
He, W., Yao, H., Mai, T., Wang, F., & Guizani, M. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3246636 · Cited by 96
“To overcome these challenges, federated learning (FL), as a compelling privacy-preserving and distributed learning paradigm, enables UAVs to conjunctively train shared AI models [6].”
BackgroundAccelerating Convergence of Federated Learning in MEC with Dynamic Community
Sun, W. Y., Zhao, Y., Ma, W., Guo, B., Xu, L., & Duong, T. Q. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3241770 · Cited by 93
“M OBILE Edge Computing (MEC), as a distributed computing paradigm, reduces the burden of backbone networks by pushing the resources to the proximity of the edge nodes [1].”
BackgroundA Collaborative Learning Framework via Federated Meta-Learning
Lin, S., Yang, G., & Zhang, J. (2020) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs47774.2020.00032 · Cited by 95
“To address the above challenges, edge computing has recently emerged, and the marriage of edge computing and AI has given rise to a new research area, namely ‘edge intelligence’ or ‘edge AI’ [1] [2].”
BackgroundA Systematic Literature Review on Distributed Machine Learning in Edge Computing
Filho, C. P., Marques, E. L., Chang, V., dos Santos, L. A., Bernardini, F., Pires, P. F., Ochi, L., & Delicato, F. C. (2022) · Italian National Conference on Sensors · doi:10.3390/s22072665 · Cited by 92
“It is a reduced DNN that achieves AlexNet-level accuracy with 50 times fewer parameters In-Edge AI [5] Federated Learning Utilizes the collaboration among devices and edge nodes to exchange the learning parameters for better training and inference of the models.”
Gong, R., Zhang, H., Li, G., & He, J. (2025) · Sensors · doi:10.3390/s25175302 · Cited by 108
“…lightweight improvements to connection establishment and maintenance mechanisms, targeted tuning of congestion control algorithms (to prevent critical command loss in bandwidth-constrained farm networks), and scenario-specific redesign or customization of transport-layer protocols [72].”
Information fusion for edge intelligence: A survey
Zhang⋆, Y., Jiang, C., Yue, B., Wan, J., & Guizani, M. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.11.018 · Cited by 91
A Review of Intelligent Connected Vehicle Cooperative Driving Development
Wang, B., Han, Y., Wang, S., Tian, D., Cai, M., Liu, M., & Wang, L. (2022) · Mathematics · doi:10.3390/math10193635 · Cited by 90
Self-citationTrust-driven reinforcement selection strategy for federated learning on IoT devices
Rjoub, G., Wahab, O. A., Bentahar, J., & Bataineh, A. S. (2022) · Computing · doi:10.1007/s00607-022-01078-1 · Cited by 88
“Federated learning has been proposed to let IoT devices in the cloud computing environment train machine learning models collaboratively without moving data from any IoT device to one central location [37].”
Enabling technologies for AI empowered 6G massive radio access networks
Shahjalal, M. A., Kim, W., Khalid, W., Moon, S., Khan, M. A., Liu, S., Lim, S., Kim, E., Yun, D.-W., Lee, J., et al. (2022) · ICT Express · doi:10.1016/j.icte.2022.07.002 · Cited by 92
Tang, L., Xie, H., Wang, X., & Bie, Z. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120860 · Cited by 89
Self-citationBig Data Privacy in Smart Farming: A Review
Amiri-Zarandi, M., Dara, R. A., Duncan, E., & Fraser, E. (2022) · Sustainability · doi:10.3390/su14159120 · Cited by 92
Security and Privacy for Mobile Edge Caching: Challenges and Solutions
Ni, J., Zhang, K., & Vasilakos, A. V. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.2000329 · Cited by 83
Generalizing AI: Challenges and Opportunities for Plug and Play AI Solutions
Ridhawi, I. A., Otoum, S., Aloqaily, M., & Boukerche, A. (2020) · IEEE Network · doi:10.1109/mnet.011.2000371 · Cited by 84
“It aims to preserve personal data privacy and adapt with heterogeneous end-devices in cellular networks [7].”
BackgroundFrom distributed machine learning to federated learning: In the view of data privacy and security
Shen, S., Zhu, T., Wu, D., Wang, W., & Zhou, W. (2021) · UTS ePRESS (University of Technology Sydney) · Cited by 82
Ismail, A. A., Khalifa, N. E. M., & El-Khoribi, R. A. (2025) · Cluster Computing · doi:10.1007/s10586-024-04893-7 · Cited by 82
“In-EdgeAI [62] handled the main optimization problems of MEC, namely computing, caching, and communication problems.”
Lu, J., Feng, L., Yang, J., Hassan, M. M., Alelaiwi, A. A., & Humar, I. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.12.024 · Cited by 83
Integrating Edge Intelligence and Blockchain: What, Why, and How
Wang, X., Ren, X., Qiu, C., Xiong, Z., Yao, H., & Leung, V. C. M. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3189962 · Cited by 83
“Then, the caching update process replaces the old data with the new in a timely manner [44], [45] to provide real-time, high-quality EI services to end users.”
MethodSelf-citationPatel, V. A., Bhattacharya, P., Tanwar, S., Gupta, R., Sharma, G., Bokoro, P. N., & Sharma, R. (2022) · IEEE Access · doi:10.1109/access.2022.3201876 · Cited by 88
“Later,Wang et al. [91] proposed an FL-based framework that integrates deep reinforcement learning with FL in mobile edge systems.”
BackgroundOptimizing Task Assignment for Reliable Blockchain-Empowered Federated Edge Learning
Kang, J., Xiong, Z., Li, X., Zhang, Y., Niyato, D., Leung, C., & Miao, C. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3055767 · Cited by 81
“These workers only upload model updates (i.e., learning parameters) to the central entity that iteratively generates a new global model through model aggregation and shares the global model with the workers [6], [7].”
BackgroundA review on federated learning towards image processing
Khokhar, F. A., Shah, J. H., Khan, M. A., Sharif, M. I., Tariq, U., & Kadry, S. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107818 · Cited by 82
Multi-exit DNN Inference Acceleration based on Multi-Dimensional Optimization for Edge Intelligence
Dong, F., Wang, H., Shen, D., Huang, Z., He, Q., Zhang, J., Wen, L., & Zhang, T. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3172402 · Cited by 79
“To meet these requirements for AI applications, deep learning with edge computing, also named edge intelligence, has been envisioned as a promising paradigm [4], [5], [6], [7].”
BackgroundFederated Learning for the Internet-of-Medical-Things: A Survey
Prasad, V. K., Bhattacharya, P., Maru, D., Tanwar, S., Verma, A., Singh, A., Tiwari, A. K., Sharma, R., Alkhayyat, A. H., Țurcanu, F.-E., et al. (2022) · Mathematics · doi:10.3390/math11010151 · Cited by 87
Zhao, Y., Zhao, J., Jiang, L., Tan, R., & Niyato, D. T. (2019) · arXiv (Cornell University) · Cited by 83
Context-Aware Multi-User Offloading in Mobile Edge Computing: a Federated Learning-Based Approach
Shahidinejad, A., Farahbakhsh, F., Ghobaei-Arani, M., Malik, M. H., & Anwar, T. (2021) · Journal of Grid Computing · doi:10.1007/s10723-021-09559-x · Cited by 73
“The paper [35] is presented based on a distributed DRL in MEC for caching and communication operations.”
MethodAI-Empowered Content Caching in Vehicular Edge Computing: Opportunities and Challenges
Javed, M. A., & Zeadally, S. (2021) · IEEE Network · doi:10.1109/mnet.011.2000561 · Cited by 77
“[15] X.”
MethodUser Access Control in Open Radio Access Networks: A Federated Deep Reinforcement Learning Approach
Cao, Y., Lien, S., Liang, Y., Chen, K., & Shen, X. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3123500 · Cited by 82
“To this end, the federated learning (FL) method [25] can be adopted to train a global DRL model using the exchanged parameters from UEs [26], [27].”
MethodLFighter: Defending against the label-flipping attack in federated learning
Jebreel, N., Domingo‐Ferrer, J., Sánchez, D., & Blanco-Justicia, A. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.11.019 · Cited by 129
Wu, Q., Wang, W., Fan, P., Fan, Q., Zhu, H., & Letaief, K. B. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3403842 · Cited by 77
“I N RECENT years, with the increasing popularity of smart devices, we have witnessed an unprecedented growth in mobile data traffic, which has imposed a heavy burden on wireless networks [1], [2], [3].”
BackgroundTowards Real-time Cooperative Deep Inference over the Cloud and Edge End Devices
Zhang, S., Li, Y., Liu, X., Guo, S., Wang, W., Wang, J., Ding, B., & Wu, D. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3397315 · Cited by 80
“Cooperative deep inference [5, 9, 12, 30, 37] is proposed in recent years to take advantage of both the high computation ability of the cloud and the local processing ability of edge end devices.”
BackgroundAir-Ground Integrated Mobile Edge Networks: A Survey
Zhang, W., Li, L., Zhang, N., Han, T., & Wang, S. (2020) · IEEE Access · doi:10.1109/access.2020.3008168 · Cited by 75
“To better solve the complex problems, artificial intelligence (AI) such as learning-based algorithm and reinforcement learning can be employed to find the near-optimal solution globally and to solve nested problem as a whole [122], [123].”
BackgroundTowards goal-oriented semantic signal processing: Applications and future challenges
Kalfa, M., Gok, M., Atalık, A., Tegin, B., Duman, T. M., & Arıkan, O. (2021) · Digital Signal Processing · doi:10.1016/j.dsp.2021.103134 · Cited by 81
Deep Learning Based Energy Efficiency Optimization for Distributed Cooperative Spectrum Sensing
He, H., & Jiang, H. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2019.1800397 · Cited by 74
“Recently, deep reinforcement learning (DRL) and federated learning techniques have been integrated to manage the resource of the mobile edge computing system [7].”
BackgroundMoving Deep Learning to the Edge
Véstias, M. P., Duarte, R. P., de Sousa, J. T., & Neto, H. C. (2020) · Algorithms · doi:10.3390/a13050125 · Cited by 79
“Federated learning [178,179] has been emerging as a training approach that allows an unbalanced distribution of workload to different computing platforms: cloud, edge, and end device.”
BackgroundOgundokun, R. O., Misra, S., Maskeliūnas, R., & Damaševičius, R. (2022) · Information · doi:10.3390/info13050263 · Cited by 79
“Wang et al. [26] develop an ‘In-Edge AI’ framework that combines FL founded on deep reinforcement learning with a MEC system to additionally enhance resource apportionment issues.”
BackgroundAccelerating Federated Learning With Data and Model Parallelism in Edge Computing
Liao, Y., Xu, Y., Xu, H., Yao, Z., Wang, L., & Qiao, C. (2023) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2023.3299851 · Cited by 77
“In order to mine and discover valuable knowledge from distributed data, many Deep Learning (DL) techniques have been applied in EC, forming a new line of research related to edge AI [6], [7].”
BackgroundShen, M., Wang, H., Zhang, B., Zhu, L., Xu, K., Li, Q. L., & Du, X. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3028110 · Cited by 73
“…brings opportunities to the traditional information-centric networks [15], [24], [25] and has been widely used in FL for edge computing to provide data confidentially, computation auditability, device authentication for cross-domain industrial IoT and participant incentives [4], [7], [26], [27].”
BackgroundNear-Online Tracking With Co-Occurrence Constraints in Blockchain-Based Edge Computing
Sheng, H., Wang, S., Zhang, Y., Yu, D., Cheng, X., Lyu, W., & Xiong, Z. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3035415 · Cited by 73
Data Protection in AI Services
Meurisch, C., & Mühlhäuser, M. (2021) · ACM Computing Surveys · doi:10.1145/3440754 · Cited by 88
A survey on federated learning in data mining
Yu, B., Mao, W., Lv, Y., Zhang, C. B. C., & Xie, Y. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1443 · Cited by 78
Wireless Network Intelligence at the Edge
Park, J., Samarakoon, S., Bennis, M., & Debbah, M. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2941458 · Cited by 92
“A similar procedure is applicable to deep Q-learning in MARL, where each agent approximates the Q values using an NN, as demonstrated in [184].”
MethodEnd-to-End Federated Learning for Autonomous Driving Vehicles
Zhang, H., Bosch, J., & Olsson, H. H. (2021) · 2021 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533808 · Cited by 84
“Wang et al. [9] propose an “In-Edge AI” framework that provides collaboration between devices and the aggregation server to exchange learning parameters for better model training in energy and computation constraint user equipment.”
BackgroundReinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence
Wei, P., Guo, K., Li, Y., Wang, J., Feng, W., Jin, S., Ge, N., & Liang, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3183647 · Cited by 76
Computation and Privacy Protection for Satellite-Ground Digital Twin Networks
Gong, Y., Yao, H., Liu, X., Bennis, M., Nallanathan, A., & Han, Z. (2024) · IEEE Transactions on Communications · doi:10.1109/tcomm.2024.3392795 · Cited by 71
“Wang et al. [28] designed an “In-Edge AI” framework to reduce system communication overhead and integrated DRL with federated learning to optimize communication, computation as well as caching resources.”
BackgroundMethodAI at the Edge: Blockchain-Empowered Secure Multiparty Learning With Heterogeneous Models
Wang, Q., Guo, Y., Wang, X., Ji, T., Yu, L., & Li, P. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2987843 · Cited by 70
“In the big data era, one of the most critical intelligent applications is multiparty learning or federated learning [1].”
BackgroundSelf-citationComputation offloading in Edge Computing environments using Artificial Intelligence techniques
Carvalho, G., Cabral, B., Pereira, V., & Bernardino, J. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103840 · Cited by 70
Tang, Q., Xie, R., Yu, F. R., Chen, T., Zhang, R., Huang, T., & Liu, Y. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3199812 · Cited by 68
Gong, Y., Yao, H., & Nallanathan, A. (2024) · IEEE Network · doi:10.1109/mnet.2024.3413543 · Cited by 68
“[6] introduced a distributed deep reinforcement learning (DRL) framework for reducing the communication overhead in edge AI scenarios.”
BackgroundAyaz, F., Sheng, Z., Tian, D., & Guan, Y. L. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3026731 · Cited by 67
Zhang, C., Xu, Y., Elahi, H., Zhang, D., Tan, Y., Chen, J., & Zhang, Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3171926 · Cited by 70
“For example, some studies exploit collaboration among edge servers and IoT devices to train models better and reduce computational loads [8], [23].”
BackgroundSecure Federated Learning by Power Control for Internet of Drones
Yao, J., & Ansari, N. (2021) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2021.3076167 · Cited by 66
Efficient federated learning for fault diagnosis in industrial cloud-edge computing
Wang, Q., Li, Q., Wang, K., Wang, H., & Zeng, P. (2021) · Computing · doi:10.1007/s00607-021-00970-6 · Cited by 66
Grape Bunch Detection at Different Growth Stages Using Deep Learning Quantized Models
Aguiar, A., Magalhães, S. A., dos Santos, F. N., de Castro, L. M. G., Pinho, T. M., Valente, J. R. P., Martins, R. C., & Boaventura‐Cunha, J. (2021) · Agronomy · doi:10.3390/agronomy11091890 · Cited by 69
“This concept is usually known as Edge Artificial Intelligence (Edge-AI) [13].”
BackgroundThe Internet of Federated Things (IoFT)
Kontar, R. A., Shi, N., Yue, X., Chung, S., Byon, E., Chowdhury, M., Jin, J. J., Kontar, W., Masoud, N., Nouiehed, M., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3127448 · Cited by 72
“computing and communication [179], [307], computational offloading [307] and reliable network communication [275].”
BackgroundA green, secure, and deep intelligent method for dynamic IoT-edge-cloud offloading scenarios
Heidari, A., Navimipour, N. J., Jamali, M. A. J., & Akbarpour, S. (2023) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2023.100859 · Cited by 66
Blockchain-Enabled Asynchronous Federated Learning in Edge Computing
Liu, Y., Qu, Y., Xu, C., Hao, Z., & Gu, B. (2021) · Sensors · doi:10.3390/s21103335 · Cited by 65
“The research [35] stated a novel method working on Non-IID data, which, through computing the aggregation bound, decreased loss function under restricted resource.”
MethodJoint Scheduling and Resource Allocation for Hierarchical Federated Edge Learning
Wen, W., Chen, Z., Yang, H. H., Xia, W., & Quek, T. Q. S. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3144140 · Cited by 65
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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