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.”
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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Claims the paper makes
0 of 25 cited sources checked · 0 supported · 0 flagged
- Findingown data§ B. Evaluation Results · p. 7
In edge caching simulation, the hit rates of edge nodes E1, E2, E3 improve and finally maintain within a certain range during DRL training.
“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.”
Quote in the paperNot a substantial claim - Findingown data§ B. Evaluation Results · p. 8
Using centralized DRL, UEs must upload the whole training data via wireless channels and thus consume more communication resources than FL.
“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).”
Quote in the paperSubstantial claim- Rests on an assumption: In the framework of FL, every client only needs to upload the update of its model.
- Findingown data§ VI. CONCLUSIONS · p. 9
In-Edge AI is evaluated and proved to have the abilities to achieve near-optimal performance.
“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.”
Quote in the paperSubstantial claim - Findingcitation§ A. Experiment Settings · p. 7
The content popularity distribution from Xender's trace can be well fitted by a Zipf distribution with α = 1.58.
“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 .”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [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.
- Findingown data§ B. Evaluation Results · p. 8
Once model merging of FL has been processed several times, the performance of DDQN with FL becomes near to that of centralized DDQN.
“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.”
Quote in the paperSubstantial claim
- Findingown data§ B. Evaluation Results · p. 8
In training process, centralized DDQN performs better than DDQN with FL at the beginning of training.
“As illustrated in Fig. 6(c), the performance of centralized DDQN is better than that of DDQN with FL at the beginning of training.”
Quote in the paperNot a substantial claim - Interpretationreasoning§ I. INTRODUCTION · p. 1
The problem in MEC resource allocation optimization is a lack of intelligence.
“In a word, the problem existed in the resource allocation optimization of the MEC system is ' lack of intelligence '.”
Quote in the paperNot a substantial claim - Premisecitation§ I. INTRODUCTION · p. 2
Federated Learning can react cognitively to the mobile communication environment and conditions of cellular networks.
“2) reacting cognitively to the mobile communication environment and conditions of cellular networks”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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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- Premisecitation§ I. INTRODUCTION · p. 2
Federated Learning can adapt well with heterogeneous UEs in a practical cellular network.
“3) adapting well with heterogeneous UEs in a practical cellular network”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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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- Interpretationcitation§ I. INTRODUCTION · p. 1
Existing optimization methods do not well address dynamics of the integrated communication and computation system.
“2) Dynamic Conditions: dynamics of the integrated communication and computation system are not well addressed;”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [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 -
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- cites [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
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- Interpretationcitation§ I. INTRODUCTION · p. 1
Existing optimization methods for MEC may suffer from uncertain inputs because key information factors are assumed given but are difficult to obtain due to variant wireless channels and privacy policies.
“1) Uncertain Inputs: they assume the that some key information factors are given as inputs, but actually some of them are difficult to obtain due to variant wireless channels and privacy policies;”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [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 -
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- cites [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 in the free run.
- Interpretationreasoning§ B. Evaluation Results · p. 7
The efficiency of handling offloaded tasks in the MEC system is improved.
“This means that the efficiency of handling offloaded tasks in the MEC system is improved.”
Quote in the paperSubstantial claim - Interpretationreasoning§ A. Integration of Federated Learning within Edge for In-Edge AI · p. 5
Using all edge nodes as an integral server for coordinating massive UEs allows UEs with relatively weak computation capability to hold a complex DRL agent.
“Therefore, we proposed to use all edge nodes in 'edge' as an integral server for coordinating massive UEs covered by them. By virtue of this scheme, UEs with relative weak computation capability could be able to hold a complex DRL agent.”
Quote in the paperNot a substantial claim- Rests on an assumption: The computation capability of a UE is relatively weak.
- Interpretationreasoning§ B. Evaluation Results · p. 8
If the client wishes to obtain the expected performance using DDQN with FL, it must take time to wait for the model merging.
“Certainly, if the client wishes to obtain the expected performance using DDQN with FL, it must take time to wait for the model merging, i.e., exploiting training results of other clients.”
Quote in the paperSubstantial claim - Interpretationcitation§ B. DRL over the MEC System for Computation Offloading · p. 4
Deep Q-Learning and its improved version, Double DQN, can train the control policy to increase the utility of UEs for long-term performance optimization.
“By taking advantage of Deep Q-Learning and its improved version, e.g., Double DQN [12], the control policy Φ = (Φ c ( Υ ) , Φ e ( Υ )) could be trained and achieves increasing the utility of UEs for the long-term performance optimization.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [12] [12] H. Van Hasselt, A. Guez, and D. Silver, 'Deep Reinforcement Learning with Double Q-Learning,' in Proceedings of the 30th AAAI Conference on Artificial Intelligence , vol. 16, 2016, pp. 2094-2100. - source
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- Interpretationreasoning§ III. IN-EDGE AI WITH FEDERATED LEARNING · p. 4
Training a DRL agent on edge nodes or remote cloud servers increases the burden of uplink wireless channels because training data is large in quantity when considering massive UEs.
“1) the training data is large in quantity when considering massive UEs, and it will increase the burden of uplink wireless channels;”
Quote in the paperSubstantial claim- Rests on an assumption: Training data is large in quantity when considering massive UEs.
- Interpretationcitation§ III. IN-EDGE AI WITH FEDERATED LEARNING · p. 4
Federated Learning can cope with unbalanced data among UEs via the FedAvg algorithm.
“Also, this challenge could be coped with the FedAvg algorithm.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [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
Not checked in the free run.
- Rests on an assumption: 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.
- Interpretationreasoning§ B. Evaluation Results · p. 8
Performing DDQN with FL is more practical in MEC systems, at least for now when the wireless resource is also the major consideration.
“Therefore, performing DDQN with FL is more practical in MEC systems, at least for now when the wireless resource is also the major consideration.”
Quote in the paperSubstantial claim- Rests on an assumption: In practice, massive training data are actually unable to be uploaded without any delay.
- Interpretationnone given§ III. IN-EDGE AI WITH FEDERATED LEARNING · p. 4
Most distributed DRL architectures usually reduce the performance of DRL agents when UEs and network states are heterogeneous.
“In addition, they usually reduce the performance of DRL agents when UEs and network states are heterogeneous.”
Quote in the paperSubstantial claim - Interpretationreasoning§ C. Practicability Discussion · p. 7
Once the model has been trained, the inferring process could be performed quickly due to the low complexity of the MainNet in DRL agents.
“Therefore, once the model has been trained, the inferring process could be performed quickly due to the low complexity of the MainNet in DRL agents.”
Quote in the paperSubstantial claim - Interpretationcitation§ A. Integration of Federated Learning within Edge for In-Edge AI · p. 5
Federated Learning enables resource-constrained edge compute devices, including UEs and edge nodes, to learn a shared model while keeping training data local.
“To summarize, FL enables resource-constrained edge to compute devices (include UEs and edge nodes) to learn a shared model, while keeping the training data local.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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
Not checked in the free run.
- Interpretationcitation§ III. IN-EDGE AI WITH FEDERATED LEARNING · p. 4
Federated Learning can meet the non-IID challenge by merging model updates with FedAvg.
“In FL, this challenge could be met by merging the updates of models with FedAvg in [11].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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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- Interpretationreasoning§ B. Pros and Cons of Federated Learning for In-Edge AI · p. 6
A trade-off between computation and communication exists in FL.
“Hence, a trade-off between computation and communication existed.”
Quote in the paperNot a substantial claim- 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.
- Premisecitation§ I. INTRODUCTION · p. 2
Federated Learning can preserve personal data privacy.
“4) preserving the personal data privacy”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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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- Interpretationreasoning§ III. IN-EDGE AI WITH FEDERATED LEARNING · p. 4
Any individual UE's training data will not be able to represent the training data of all UEs.
“any individual training data of a UE will not be able to represent the training data of all UEs.”
Quote in the paperSubstantial claim
- May combine two claims. 25
- Classified as probably not a substantial claim. 21
May combine two claims.
Findingown data§ In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning · p. 1The proposed In-Edge AI framework has near-optimal performance but relatively low learning overhead, and the system is cognitive and adaptive to mobile communication systems.
“'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.”
Quote in the paperSubstantial claim- Also stated in: § I. INTRODUCTION · p. 2 · plain
May combine two claims.
Findingown data§ B. Evaluation Results · p. 7In edge caching, DDQN with FL achieves hit rates near close to centralized DDQN and outperforms LRU, LFU, and FIFO.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ B. Evaluation Results · p. 7In computation offloading simulation, the average utilities of UEs m1, m2, m3 increase with decreasing training loss and maintain within a certain range at last.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ B. Evaluation Results · p. 7In computation offloading, DDQN with FL achieves near performance of centralized DDQN and is superior to mobile execution, edge node execution, and greedy execution.
“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;”
Quote in the paperSubstantial claimMay combine two claims.
Findingreasoning§ B. Pros and Cons of Federated Learning for In-Edge AI · p. 6FL could not outperform the centralized DL trained model, but could still achieve the near best performance.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Premisecitation§ I. INTRODUCTION · p. 1Edge nodes can handle computation and content requests of UEs, improving QoS of MNOs and QoE of UEs and relieving backbone and cloud load.
“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].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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. -
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May combine two claims.
Premisecitation§ I. INTRODUCTION · p. 2Federated Learning can train DRL agents in a distributed manner while largely reducing the amount of data uploaded via the wireless uplink channel.
“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”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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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May combine two claims.
Premisereasoning§ I. INTRODUCTION · p. 1MEC circumvents long propagation delays from mobile devices to remote cloud infrastructures and can support latency-critical mobile and IoT applications.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationcitation§ I. INTRODUCTION · p. 1Most existing optimization methods do not consider the long-term effect of current decisions except Lyapunov optimization, so in a highly time-varying MEC system they are optimal or close-to-optimal only for a snapshot.
“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.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [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 in the free run.
- cites [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 in the free run.
May combine two claims.
Interpretationreasoning§ I. INTRODUCTION · p. 2[7] is based on Q-Learning, which is not feasible for a practical MEC system where the state-action space is tremendous.
“But [7] is based on Q-Learning, which is not feasible for the practical MEC system where the stateaction space is tremendous”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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. - source
Not checked in the free run.
Classified as probably not a substantial claim.
Premisecitation§ I. INTRODUCTION · p. 1Mobile Edge Computing (MEC) is a paradigm that eases the burden of backbone networks by pushing computation/storage resources to the proximity of User Equipments (UEs).
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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.
May combine two claims.
Interpretationreasoning§ I. INTRODUCTION · p. 2Prior works have not considered how training data shall be gathered, where the RL agent should be placed and trained, how the update process should be proceeded and collaborated, or privacy protection of training data.
“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”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ I. INTRODUCTION · p. 1A recent trend is to optimize wireless communication by AI techniques, including PHY, data link layer, and traffic control.
“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].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [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
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- cites [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
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- cites [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
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Classified as probably not a substantial claim.
Premisecitation§ I. INTRODUCTION · p. 2[8] focuses on vehicular networks.
“the focus of [8] is on vehicular networks.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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. -
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May combine two claims.
Interpretationreasoning§ III. IN-EDGE AI WITH FEDERATED LEARNING · p. 4Training a DRL agent on a UE individually takes a long time or is impossible on large-scale data because UE computation capability is relatively weak.
“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;”
Quote in the paperSubstantial claim- Rests on an assumption: The computation capability of a UE is relatively weak.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Interpretationreasoning§ B. Evaluation Results · p. 8This will cause more energy cost on UEs or burden the computation of both UEs and edge nodes.
“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.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone given§ A. Accelerating AI Tasks by Edge Communication and Computing Systems · p. 8This is the reverse direction of the aforementioned Federated Learning, but also a kind of application of the 'In-Edge AI'.
“Interestingly, this is the reverse direction of the aforementioned Federated Learning, but also a kind of application of the 'In-Edge AI'.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone given§ III. IN-EDGE AI WITH FEDERATED LEARNING · p. 4If training data is transformed for privacy, server-side proxy data is less relevant than on-device data.
“3) if training data is transformed for privacy consideration, server-side proxy data is less relevant than on-device data.”
Quote in the paperNot a substantial claim- Rests on an assumption: If training data is transformed for privacy, server-side proxy data is less relevant than on-device data.
Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationreasoning§ C. Practicability Discussion · p. 6Deploying the DRL model directly with random weights paralyzes the MEC system because the DRL model could only make random decisions at preliminary exploration.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationcitation§ C. Practicability Discussion · p. 7By incorporating work [14], the requirement on computation capacity of UEs could be relaxed along with decreasing the inferring delay.
“By incorporating this work, the requirement on computation capacity of UEs could be relaxed along with decreasing the inferring delay.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [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. -
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Interpretationreasoning§ III. IN-EDGE AI WITH FEDERATED LEARNING · p. 4Federated Learning handles limited communication because using additional computation can decrease the communication rounds needed to train a model, and FL asks only a part of clients per round to upload updates, which handles unpredictable offline clients.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: UEs are often unpredictably off-line or allocated with poor communication resources.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone given§ A. Integration of Federated Learning within Edge for In-Edge AI · p. 4Each edge node can support AI tasks on a system level dynamically, for global optimization and balancing of the whole MEC system.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationcitation§ III. IN-EDGE AI WITH FEDERATED LEARNING · p. 4Most distributed DRL architectures cannot handle unbalanced and non-IID data or cope with privacy issues.
“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].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone given§ A. Integration of Federated Learning within Edge for In-Edge AI · p. 4With Federated Learning, edge nodes equipped with AI computation can combine the cloud and massive UEs to form a powerful AI entity with strong cognitive ability.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationcitation§ III. IN-EDGE AI WITH FEDERATED LEARNING · p. 4Federated Learning uploads only the minimal update necessary to improve the DRL agent, and secure aggregation and differential privacy can be applied to avoid privacy-sensitive data in local updates.
“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.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone given§ III. IN-EDGE AI WITH FEDERATED LEARNING · p. 4Training data uploaded to edge nodes or cloud is privacy-sensitive and may cause potential privacy accidents.
“2) the training data which should be uploaded to edge nodes or cloud is privacy-sensitive, and it might cause potential privacy accidents;”
Quote in the paperNot a substantial claim- Rests on an assumption: Training data that should be uploaded to edge nodes or cloud is privacy-sensitive.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationreasoning§ C. Practicability Discussion · p. 7Based on pre-established DRL, the requirement of training is alleviated, i.e., the DRL agent could reach the satisfied accuracy level after a small number of mini-batches or even directly inferring.
“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.”
Quote in the paperNot a substantial claim- Rests on an assumption: Learning takes long time of training as well as inferring according to the required accuracy level.
Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationreasoning§ C. Practicability Discussion · p. 7When the wireless environment or the pattern of UEs' requests is changed, the UE could take advantage of the established DRL to adjust neural networks in its DRL model.
“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.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Interpretationreasoning§ B. Pros and Cons of Federated Learning for In-Edge AI · p. 6Powerful UEs could decide to perform more mini-batches in training for further decreasing the communication cost and vice versa.
“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.”
Quote in the paperNot a substantial claim- 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.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationreasoning§ B. Pros and Cons of Federated Learning for In-Edge AI · p. 5The performance of In-Edge AI shall not be easily affected by the unbalanced data and sometimes the poor communication environment.
“Consequently, the performance of In-Edge AI shall not be easily affected by the unbalanced data and sometimes the poor communication environment.”
Quote in the paperNot a substantial claim- 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.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone given§ C. Practicability Discussion · p. 6If using transfer learning, there shall be less time or computation consumption on the side of training.
“However, if using transfer learning, there shall be less time or computation consumption on the side of training.”
Quote in the paperNot a substantial claim- Rests on an assumption: Learning takes long time of training as well as inferring according to the required accuracy level.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationreasoning§ C. Practicability Discussion · p. 6The problem of deploying DRL with random weights may be solved if the DL model is not trained from scratch.
“Nonetheless, this may be solved if the DL model is not trained from scratch.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone given§ C. Incentive and Business Model of In-Edge AI · p. 9In-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.
“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.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone given§ A. Accelerating AI Tasks by Edge Communication and Computing Systems · p. 8DRL can be treated as a type of specialized edge computing task at the service-level.
“DRL can be treated as a type of specialized edge computing task at the service-level.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationreasoning§ C. Practicability Discussion · p. 6The established DRL model could be distributed to mobile UEs.
“Then, the established DRL model could be distributed to mobile UEs.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationreasoning§ B. Pros and Cons of Federated Learning for In-Edge AI · p. 5FL's ability of handling non-IID data allows massive UEs in different wireless environments to train their own DL model without concerning about the negative effect.
“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.”
Quote in the paperNot a substantial claim- 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.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationreasoning§ B. Evaluation Results · p. 8The coordinating server in FL only executes tasks of merging updates instead of taking over the whole training, so the computation load of clients is inevitably heavier on account of the local training process.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationreasoning§ III. IN-EDGE AI WITH FEDERATED LEARNING · p. 4It is only practical to apply distributed DRL in the MEC system because there is not enough time and data for training a DRL agent in every UE and edge node.
“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.”
Quote in the paperNot a substantial claim- 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?
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.
“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 [9] an adaptive control scheme was proposed to adapt the global FL aggregation frequency.
“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]
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.
“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]
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× · see citing works
“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 25,263 · see citing works
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× · see citing works
“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 8,821 · see citing works
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× · see citing works
“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× · see citing works
“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 1,089 · cited 1× · see citing works
“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× · see citing works
“, 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× · see citing works
“, 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 · see citing works
He, Y., Zhao, N., & Yin, H. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2760281 · Cited by 569 · cited 2× · see citing works
“Particularly, related studies on edge computing and caching, such as [7] [8], have shown that reinforcement”
BackgroundMethodEdge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy
Li, E., Zhou, Z., & Chen, X. (2018) · Proceedings of the 2018 Workshop on Mobile Edge Communications · doi:10.1145/3229556.3229562 · Cited by 385 · cited 2× · see citing works
“Besides, our work [14] aims to meet the challenge of running DL on resource-constrained mobile devices.”
BackgroundMethodSelf-citationOptimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities
Sadeghi, A., Sheikholeslami, F., & Giannakis, G. B. (2018) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2787979 · Cited by 257 · cited 2× · see citing works
“Particularly, related studies on edge computing and caching, such as [7] [8], have shown that reinforcement”
BackgroundMethodTang, F., Mao, B., Fadlullah, Z. M., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2018) · IEEE Wireless Communications · doi:10.1109/mwc.2017.1700244 · Cited by 247 · cited 1× · see citing works
“, 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].”
MethodLi, X., Wang, X., Wan, P.-J., Han, Z., & Leung, V. C. M. (2018) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2018.2844658 · Cited by 140 · cited 1× · see citing works
“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.”
MethodSelf-citationWhen Mobile Blockchain Meets Edge Computing: Challenges and Applications.Preprint
Xiong, Z., Zhang, Y., Niyato, D. T., Wang, P., & Han, Z. (2017) · arXiv (Cornell University) · Cited by 23 · see citing works
When Mobile Blockchain Meets Edge ComputingPreprint
Xiong, Z., Zhang, Y. A., Niyato, D. T., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.05938 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- 2014, Mobile Edge Computing – Introductory Technical White Paper
Cited by
Sorted by citations
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565 · see citing works
“To account for the dynamic and time-varying conditions in a MEC system, the authors in [32] propose the use of DRL with FL to optimize caching and computation offloading”
BackgroundMethodResultEdge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2918951 · Cited by 2,455 · see citing works
“Indeed, the marriage of edge computing and AI has given rise to a new research area, namely, “edge intelligence (EI)” or “edge AI” [9], [10].”
BackgroundSelf-citationFederated Learning With Differential Privacy: Algorithms and Performance Analysis
Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., & Vincent Poor, H. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2988575 · Cited by 2,400 · see citing works
“However, FL also poses several key challenges, such as private information leakage, expensive communication costs between servers and clients, and device variability [9]–[15].”
BackgroundA Joint Learning and Communications Framework for Federated Learning Over Wireless Networks
Chen, M., Yang, Z., Saad, W., Yin, C., Poor, H. V., & Cui, S. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3024629 · Cited by 1,547 · see citing works
“First, the works in [7], [19], and [22] only provided a high-level exposition of the challenges of communication in FL.”
BackgroundResultSelf-citationA review of applications in federated learning
Li, L., Fan, Y., Tse, M., & Lin, K.-Y. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106854 · Cited by 1,559 · see citing works
Federated Learning for Healthcare Informatics
Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-020-00082-4 · Cited by 1,542 · see citing works
“Reinforcement learning and round robin learning are widely used to manage the communication and computation resources [5, 46, 106, 114].”
BackgroundFederated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503 · see citing works
“[67] Edge data caching CFL DRL User equipments Edge/cloud server An edge caching scheme with DRL and FL.”
MethodA Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., & He, B. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3124599 · Cited by 1,407 · see citing works
Kang, J., Xiong, Z., Niyato, D. T., Xie, S., & Zhang, J. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2940820 · Cited by 961 · see citing works
“[16] combined deep reinforcement learning and the federated learning framework into mobile edge systems to upgrade the federated learning architecture to achieve mobile edge computing, caching, and communication optimization.”
BackgroundFederated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. (2021) · Deakin Research Online (Deakin University) · Cited by 889 · see citing works
Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
Khan, L. U., Saad, W., Han, Z., Hossain, E., & Hong, C. S. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3090430 · Cited by 892 · see citing works
Abdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3030072 · Cited by 796 · see citing works
“To start with, FL has been implemented in edge systems while integrating deep reinforcement learning in [74].”
BackgroundMethodA Survey on Federated Learning for Resource-Constrained IoT Devices
Imteaj, A., Thakker, U., Wang, S., Li, J., & Amini, M. H. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3095077 · Cited by 774 · see citing works
“Besides, Wang et al. [11] designed a framework to exchange learning parameters of the clients through the collaboration for generating better local training models.”
Scheduling Policies for Federated Learning in Wireless Networks
Yang, H. H., Liu, Z., Quek, T. Q. S., & Poor, H. V. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2944169 · Cited by 640 · see citing works
“Particularly, when the amount of training time is limited, solutions are taken by adapting the number of locally computing steps to the variance of global gradient [11], [15], [16], or scheduling the maximum number of UEs in a given time frame [10].”
BackgroundFederated learning meets blockchain in edge computing: Opportunities and challenges
Nguyen, D. C., Ding, M., Pham, Q.-V., Pathirana, P. N., Le, L. B., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3072611 · Cited by 661 · see citing works
Yin, X., Zhu, Y., & Hu, J. (2022) · ACM Computing Surveys · doi:10.1145/3460427 · Cited by 638 · see citing works
“Recently, several research studies on FL in mobile edge computing have been proposed [49, 105, 116, 140, 189, 190, 201, 220].”
BackgroundPrivacy-Preserving Blockchain-Based Federated Learning for IoT Devices
Zhao, Y., Zhao, J., Jiang, L., Tan, R., Niyato, D., Li, Z., Lyu, L., & Liu, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3017377 · Cited by 613 · see citing works
“In addition, a few studies have combined deep learning or FL with edge computing [44]–[47].”
BackgroundResource Scheduling in Edge Computing: A Survey
Luo, Q., Hu, S., Li, C., Li, G., & Shi, W. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3106401 · Cited by 588 · see citing works
“computing, the authors in [135] integrated the DRL method and FL method in edge computing and proposed an In-EdgeAI framework, where the parameters of the training model are exchanged between end-devices and edge node to better”
BackgroundMethodSingh, R., & Gill, S. S. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.02.004 · Cited by 572 · see citing works
Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions
Khan, L. U., Saad, W., Niyato, D. T., Han, Z., & Hong, C. S. (2022) · IEEE Communications Magazine · doi:10.1109/mcom.001.21143 · Cited by 559 · see citing works
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges
Chen, M., Gündüz, D., Huang, K., Saad, W., Bennis, M., Feljan, A. V., & Poor, H. V. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3118346 · Cited by 558 · see citing works
Self-citationFederated learning review: Fundamentals, enabling technologies, and future applications
Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., & Jararweh, Y. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103061 · Cited by 556 · see citing works
Edge Machine Learning for AI-Enabled IoT Devices: A Review
Merenda, M., Porcaro, C., & Iero, D. (2020) · Sensors · doi:10.3390/s20092533 · Cited by 523 · see citing works
“In-edge AI [6] is a framework which allows better collaboration among devices and edge nodes to exchange the learning parameters for a better training and inference of the model.”
BackgroundFederated Learning for Edge Networks: Resource Optimization and Incentive Mechanism
Khan, L. U., Pandey, S. R., Tran, N. H., Saad, W., Han, Z., Nguyen, M. N. H., & Hong, C. S. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900649 · Cited by 506 · see citing works
Wahab, O. A., Mourad, A., Otrok, H., & Taleb, T. (2021) · IEEE Communications Surveys and Tutorials · doi:10.1109/comst.2021.3058573 · Cited by 444 · see citing works
“In [98], the authors design an inedge federated learning framework that integrates together deep reinforcement, federated learning and mobile edge computing.”
BackgroundEdge-Computing-Enabled Smart Cities: A Comprehensive Survey
Khan, L. U., Yaqoob, I., Tran, N. H. K., Kazmi, S. M. A., Dang, T. N., & Hong, C. S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2987070 · Cited by 509 · see citing works
“However, the enabling of federated learning at the edge requires on-demand computational resources [93].”
InfluentialDecentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing
Qu, Y., Gao, L., Luan, T. H., Xiang, Y., Yu, S., Li, B., & Zheng, G. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2977383 · Cited by 451 · see citing works
“Federated learning is a potentially promising tool, which is proposed as a decentralized learning scheme where the local data are distributed to the data owner [33], [34].”
BackgroundA state-of-the-art survey on solving non-IID data in Federated Learning
Ma, X., Zhu, J., Lin, Z., Chen, S., & Qin, Y. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.05.003 · Cited by 404 · see citing works
Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399 · see citing works
“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].”
BackgroundFederated Learning for Vehicular Internet of Things: Recent Advances and Open Issues
Du, Z., Wu, C., Yoshinaga, T., Yau, K.-L. A., Ji, Y., & Li, J. (2020) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2020.2992630 · Cited by 393 · see citing works
“[33] propose a FL framework for MEC systems for optimizing computing, caching and communications.”
6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
Khan, L. U., Yaqoob, I., Imran, M. A., Han, Z., & Hong, C. S. (2020) · IEEE Access · doi:10.1109/access.2020.3015289 · Cited by 389 · see citing works
“Over 2, 000 configurable parameters are expected in a typical 6G smart device [31].”
BackgroundJoint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
Shi, W., Zhou, S., Niu, Z., Jiang, M., & Geng, L. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3025446 · Cited by 381 · see citing works
Federated Learning for 6G: Applications, Challenges, and Opportunities
Yang, Z.-H., Chen, M., Wong, K.-K., Poor, H., & Cui, S. (2021) · Engineering · doi:10.1016/j.eng.2021.12.002 · Cited by 356 · see citing works
Self-citationYu, S., Chen, X., Zhou, Z., Gong, X., & Wu, D. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3026589 · Cited by 362 · see citing works
Self-citationA Survey on 5G Millimeter Wave Communications for UAV-Assisted Wireless Networks
Zhang, L., Zhao, H. M., Hou, S., Zhao, Z. W., Xu, H., Wu, X., Wu, Q., & Zhang, R. (2019) · IEEE Access · doi:10.1109/access.2019.2929241 · Cited by 352 · see citing works
Federated Learning in Edge Computing: A Systematic Survey
Abreha, H. G., Hayajneh, M., & Serhani, M. A. (2022) · Sensors · doi:10.3390/s22020450 · Cited by 366 · see citing works
“, the probability distribution of contents requested from all clients) [134], there is a common interest of all users in the network.”
BackgroundMethodInfluentialOn Safeguarding Privacy and Security in the Framework of Federated Learning
Ma, C., Li, J., Ding, M., Yang, H. H., Shu, F., Quek, T. Q. S., & Poor, H. V. (2020) · IEEE Network · doi:10.1109/mnet.001.1900506 · Cited by 318 · see citing works
“In a concurrent development, artificial intelligence (AI) has revolutionized the ways that information is extracted with ground breaking successes in areas such as computer vision, natural language processing, voice recognition, etc [1].”
BackgroundSecurity and Privacy on 6G Network Edge: A Survey
Mao, B., Liu, J., Wu, Y., & Kato, N. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3244674 · Cited by 323 · see citing works
“Moreover, edge intelligence can improve the security of edge caching and edge computing, while edge caching and edge computing can provide the required storage and computing resource for intelligent security and privacy solutions [38], [39].”
BackgroundDeep Reinforcement Learning for Internet of Things: A Comprehensive Survey
Chen, W., Qiu, X., Cai, T., Dai, H., Zheng, Z., & Zhang, Y. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3073036 · Cited by 318 · see citing works
“In addition, the computation offloading problem in blockchain-empowered IoT can also be found in [166] and [167].”
BackgroundLim, W. Y. B., Ng, J. S., Xiong, Z., Jin, J., Zhang, Y., Niyato, D. T., Leung, C., & Miao, C. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3096076 · Cited by 320 · see citing works
“This has enabled several practical applications, e.g., in the development of next-word-prediction models for text messaging [6], healthcare [7], Unmanned Aerial Vehicles (UAV) sensing [8], and mobile edge computing [9].”
Federated Learning in Smart City Sensing: Challenges and Opportunities
Jiang, J. C., Kantarcı, B., Oktuğ, S., & Soyata, T. (2020) · Sensors · doi:10.3390/s20216230 · Cited by 322 · see citing works
“The goal is to design a machine learning framework that meets data privacy, security and regulatory requirements so that artificial intelligence systems can leverage the use of their data more efficiently and accurately [121].”
Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3329061 · Cited by 315 · see citing works
“For energy-efficient computation offloading decisions within MEC systems, deep reinforcement learning techniques and FL frameworks with mobile edge systems step in, conserving IoT device energy while ensuring service quality [97].”
An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee
Huang, T., Lin, W., Wu, W., He, L., Li, K., & Zomaya, A. Y. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3040887 · Cited by 294 · see citing works
Wu, H., Wolter, K., Jiao, P., Deng, Y., Zhao, Y., & Xu, M. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3033521 · Cited by 289 · see citing works
Lu, Y., Huang, X., Zhang, K., Maharjan, S., & Zhang, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3015772 · Cited by 285 · see citing works
Fusion of Federated Learning and Industrial Internet of Things: A survey
Boobalan, P., Ramu, S. P., Pham, Q., Dev, K., Pandya, S., Maddikunta, P. K. R., Gadekallu, T. R., & Huynh‐The, T. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109048 · Cited by 321 · see citing works
Precision Irrigation Management Using Machine Learning and Digital Farming Solutions
Abioye, A. E., Hensel, O., Esau, T. J., Elijah, O., Abidin, M. S. Z., Ayobami, A. S., Omosun, Y., & Nasirahmadi, A. (2022) · AgriEngineering · doi:10.3390/agriengineering4010006 · Cited by 283 · see citing works
“Federated learning is a procedure that allows devices such as nodes, sensors, and local clients to train and share prediction models collaboratively, but the individual devices retain their data [158].”
BackgroundZhang, X., Peng, M., Yan, S., & Sun, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2962715 · Cited by 282 · see citing works
Du, J., Yu, F. R., Lu, G., Wang, J., Jiang, J., & Chu, X. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3003449 · Cited by 279 · see citing works
“(MECSs) [12], [13], and consequently offers an opportunity to tackle the last two challenges [14].”
BackgroundAslanpour, M. S., Gill, S. S., & Toosi, A. N. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100273 · Cited by 289 · see citing works
“With the advances in Deep Learning and the idea of Federated Machine Learning at the edge, conventual metrics require to be reexamined in the context of edge computing [28].”
BackgroundToward an Automated Auction Framework for Wireless Federated Learning Services Market
Jiao, Y., Wang, P., Niyato, D. T., Lin, B., & Kim, D. I. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.2994639 · Cited by 275 · see citing works
Edge Intelligence: Empowering Intelligence to the Edge of Network
Xu, D., Li, T., Li, Y., Su, X. J., Tarkoma, S., Jiang, T., Crowcroft, J., & Hui, P. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3119950 · Cited by 281 · see citing works
Deep Learning-based Anomaly Detection in Cyber-physical Systems
Luo, Y., Xiao, Y., Cheng, L., Peng, G., & Yao, D. D. (2021) · ACM Computing Surveys · doi:10.1145/3453155 · Cited by 332 · see citing works
“Another solution is known as edge computing [103], which is used”
BackgroundEdge Intelligence and Internet of Things in Healthcare: A Survey
Amin, S. U., & Hossain, M. S. (2020) · IEEE Access · doi:10.1109/access.2020.3045115 · Cited by 264 · see citing works
“In fact, edge intelligence cannot be restricted to ML or DL-based techniques only [22], but it is now being researched for every domain of smart healthcare involving IR 4.”
BackgroundLiu, B., Wang, L., & Liu, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2931179 · Cited by 242 · see citing works
“Different from the traditional joint learning method where multiple edges are learning at the same time, LFRLA adopts the method of first training then mergingto reduce the dependence on the quality of communication[25][26].”
MethodScheduling for Cellular Federated Edge Learning With Importance and Channel Awareness
Ren, J., He, Y.-H., Wen, D.-Z., Yu, G., Huang, K., & Guo, D. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3015671 · Cited by 14 · see citing works
“To enable rapid access to the large amount of real-time data generated by massive distributed edge devices, a new paradigm of computing, called edge learning has emerged by migrating learning from central clouds towards the edge of wireless networks [5]–[7].”
BackgroundDeep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT
Zhang, P., Wang, C., Jiang, C., & Han, Z. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3064351 · Cited by 238 · see citing works
“In addition, there were some typical representatives who applied FL to solve different actual network problems [28]–[32], which had shown good results in solving the computing, caching and communication, and IIoT problems of the intelligent mobile edge.”
BackgroundSelf-citationLee, S., & Choi, D. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3035451 · Cited by 237 · see citing works
Client Selection for Federated Learning With Non-IID Data in Mobile Edge Computing
Zhang, W., Wang, X., Zhou, P., Wu, W., & Zhang, X. (2021) · IEEE Access · doi:10.1109/access.2021.3056919 · Cited by 234 · see citing works
“For example, [14] uses FL to train deep reinforcement learning agents in a distributed manner.”
MethodSelf-citationFMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC
Zeng, R., Zhang, S., Wang, J., & Chu, X. (2020) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs47774.2020.00094 · Cited by 225 · see citing works
“Another interesting work is given in [32], where both deep reinforcement learning and federated learning are employed to optimize edge computing, caching and communication in MEC.”
BackgroundSurvey on computation offloading in UAV-Enabled mobile edge computing
Huda, S. M. A., & Moh, S. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103341 · Cited by 225 · see citing works
AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient Federated Learning
Deng, Y., Lyu, F., Ren, J., Wu, H., Zhou, Y., Zhang, Y., & Shen, X. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3134647 · Cited by 220 · see citing works
“motes the advancement of modern artificial intelligent applications [1], [2], [3], [4].”
BackgroundBlockchain for Edge of Things: Applications, Opportunities, and Challenges
Gadekallu, T. R., Pham, Q.-V., Nguyen, D. C., Maddikunta, P. K. R., Deepa, N., Prabadevi, B., Pathirana, P. N., Zhao, J., & Hwang, W.-J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3119639 · Cited by 217 · see citing works
Network Slicing: Recent Advances, Taxonomy, Requirements, and Open Research Challenges
Khan, L. U., Yaqoob, I., Tran, N. H. K., Han, Z., & Hong, C. S. (2020) · IEEE Access · doi:10.1109/access.2020.2975072 · Cited by 215 · see citing works
“It is expected that there will be more than 2000 tunable parameters in a typical intelligent 5G transceiver [98].”
BackgroundMethodWhen Machine Learning Meets Privacy in 6G: A Survey
Sun, Y., Liu, J., Wang, J., Cao, Y., & Kato, N. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3011561 · Cited by 211 · see citing works
“Wang et al. propose using federated learning to enable intelligence in mobile edge computing [175].”
BackgroundApplications of federated learning in smart cities: recent advances, taxonomy, and open challenges
Zheng, Z., Zhou, Y., Sun, Y., Wang, Z., Liu, B., & Li, K. (2021) · Connection Science · doi:10.1080/09540091.2021.1936455 · Cited by 218 · see citing works
“More-over, several insurance companies are concerned about protecting their data, which they are unwilling to share with other entities (G. Wang et al., 2019).”
BackgroundMethodInfluentialFederated Learning via Intelligent Reflecting Surface
Wang, Z., Qiu, J., Zhou, Y., Shi, Y., Fu, L., Chen, W., & Letaief, K. B. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3099505 · Cited by 205 · see citing works
“With the further rise of edge computing and Internet of Things (IoT), there emerges a new AI paradigm, named edge AI [1]–[3], which pushes the AI frontier from the cloud center to the network edge.”
Background6G-Enabled Edge AI for Metaverse: Challenges, Methods, and Future Research Directions
Chang, L., Zhang, Z., Li, P., Xi, S., Guo, W., Shen, Y., Xiong, Z., Kang, J., Niyato, D. T., Qiao, X., et al. (2022) · Journal of Communications and Information Networks · doi:10.23919/jcin.2022.9815195 · Cited by 204 · see citing works
Li, D., Han, D., Weng, T., Zheng, Z., Li, H., Liu, H., Castiglione, A., & Li, K. (2021) · Soft Computing · doi:10.1007/s00500-021-06496-5 · Cited by 208 · see citing works
“…2020; Rei-sizadeh et al. 2020; Li et al. 2020; Niknam et al. 2020), edge computing(Wangetal.2019;Dokuetal.2021;Luetal.2020; Fantacci and Picano 2020; Wang et al. 2019; Li et al. 2020; Lim et al. 2020), health care (Rieke et al. 2020; Bogdanova et al. 2020; Zerka et al. 2020), Internet of Things…”
BackgroundAn Incentive Mechanism for Federated Learning in Wireless Cellular Networks: An Auction Approach
Le, T. H. T., Tran, N. H., Tun, Y. K., Nguyen, M. N. H., Pandey, S. R., Han, Z., & Hong, C. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3062708 · Cited by 202 · see citing works
“The study in [19] recommended combining deep reinforcement learning (DRL) and FL frameworks with mobile edge systems to optimize computing, caching and communication.”
BackgroundDu, J., Cheng, W., Lu, G., Cao, H., Chu, X., Zhang, Z., & Wang, J. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3068340 · Cited by 199 · see citing works
A Survey of Blockchain and Artificial Intelligence for 6G Wireless Communications
Zuo, Y., Guo, J., Gao, N., Zhu, Y., Jin, S., & Li, X. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3315374 · Cited by 206 · see citing works
“In addition, references [155]– [158] also successfully employed DRL algorithms in the joint optimization problem of caching and computing resources, indicating that DRL has a strong application prospect in”
MethodFive Facets of 6G: Research Challenges and Opportunities
Shen, L.-H., Feng, K., & Hanzo, L. (2022) · ACM Computing Surveys · doi:10.1145/3571072 · Cited by 192 · see citing works
“Given the rapid evolution of virtualized SDNs, it appears promising to adopt AI techniques for efficiently assigning resources both to CUs as well as to edge servers and flexibly manage the resultant mobile network [237].”
Liu, L., Feng, J., Pei, Q., Chen, C., Ming, Y., Shang, B., & Dong, M. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3048345 · Cited by 189 · see citing works
“Because data may contain sensitive and private information, e.g., personal behavior patterns and clinical data, careless data sharing will cause information leakage [17].”
BackgroundA Privacy-Preserving Federated Learning for Multiparty Data Sharing in Social IoTs
Yin, L., Feng, J., Xun, H., Sun, Z., & Cheng, X. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3074185 · Cited by 191 · see citing works
Blockchain-Based Edge Computing Resource Allocation in IoT: A Deep Reinforcement Learning Approach
He, Y., Wang, Y., Qiu, C., Lin, Q., Li, J., & Ming, Z. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3035437 · Cited by 188 · see citing works
Ren, J., Wang, H., Hou, T., Zheng, S., & Tang, C. (2019) · IEEE Access · doi:10.1109/access.2019.2919736 · Cited by 185 · see citing works
Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface
Yang, K., Shi, Y., Zhou, Y., Yang, Z., Fu, L., & Chen, W. (2020) · IEEE Network · doi:10.1109/mnet.011.2000045 · Cited by 184 · see citing works
“…solution for privacy-sensitive and low-latency intelligent IoT applications K. Yang, Y. Shi, Y. Zhou and Z. Yang are with ShanghaiTech University; L. Fu is with Xiamen University; Wei Chen is with Tsinghua University. and has the capability of harnessing distributed computation resources [4], [5].”
BackgroundHu, S., Chen, X., Ni, W., Hossain, E., & Wang, X. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3086014 · Cited by 179 · see citing works
Self-citationAli, M., Karimipour, H., & Tariq, M. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102355 · Cited by 177 · see citing works
Resource Allocation With Edge Computing in IoT Networks via Machine Learning
Liu, X., Yu, J., Wang, J., & Gao, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2970110 · Cited by 175 · see citing works
“In [24], an In-Edge AI has been evaluated and could achieve near-optimal performance by investigating the scenarios of edge caching and computation offloading in MEC systems.”
BackgroundIntrusion Detection for Wireless Edge Networks Based on Federated Learning
Chen, Z., Lv, N., Liu, P., Fang, Y. K., Chen, K., & Pan, W. M. (2020) · IEEE Access · doi:10.1109/access.2020.3041793 · Cited by 172 · see citing works
Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges
Wang, S., Qureshi, M. A., Miralles-Pechuán, L., Huynh-The, T., Gadekallu, T. R., & Liyanage, M. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3386872 · Cited by 170 · see citing works
AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges
Al-Doghman, F., Moustafa, N., Khalil, I., Sohrabi, N., Tari, Z., & Zomaya, A. Y. (2022) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2022.3155447 · Cited by 179 · see citing works
Anonymous and Privacy-Preserving Federated Learning With Industrial Big Data
Zhao, B., Fan, K., Yang, K., Wang, Z., Li, H., & Yang, Y. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3052183 · Cited by 169 · see citing works
“Wang et al. [20] integrate deep reinforcement learning and FL with mobile edge systems.”
BackgroundBringing ai to edge: from deep learning’s perspective
Liu, D., Kong, H., Luo, X., Liu, W., & Subramaniam, R. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.04.141 · Cited by 171 · see citing works
“Recently, few works study to employ incremental learning [227] and federated learning [230] [231] with edge systems, but the research in this context is still in its infancy.”
BackgroundFederated Learning With Non-IID Data in Wireless Networks
Zhao, Z., Feng, C., Hong, W., Jiang, J., Jia, C., Quek, T. Q. S., & Peng, M. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3108197 · Cited by 165 · see citing works
Leng, J., Zhu, X., Huang, Z., Xu, K., Liu, Z., Liu, Q., & Chen, X. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3257172 · Cited by 165 · see citing works
“In the system, proactive control refers to demand-capability matching, while reactive control (In-Edge [49]) refers to collaborative process control.”
BackgroundSelf-citationFederated Learning for Edge Computing: A Survey
Brecko, A., Kajáti, E., Koziorek, J., & Zolotová, I. (2022) · Applied Sciences · doi:10.3390/app12189124 · Cited by 162 · see citing works
“Wang et al. [108] also give another example of FL usage in mobile devices.”
BackgroundMethodIntelligent Cooperative Edge Computing in Internet of Things
Gong, C., Lin, F., Gong, X., & Lu, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2986015 · Cited by 158 · see citing works
“Wang et al. [11] proposed their in-edge AI by applying deep reinforcement learning techniques in the federated learning framework for edge computing.”
BackgroundMethodNie, Y., Zhao, J., Gao, F., & Yu, F. R. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3118446 · Cited by 160 · see citing works
Mhaisen, N., Abdellatif, A. A., Mohamed, A. M. S., Erbad, A., & Guizani, M. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3053588 · Cited by 159 · see citing works
AI business model: an integrative business approach
Mishra, S., & Tripathi, A. (2021) · Journal of Innovation and Entrepreneurship · doi:10.1186/s13731-021-00157-5 · Cited by 188 · see citing works
Applications of Federated Learning; Taxonomy, Challenges, and Research Trends
Shaheen, M., Farooq, M. S., Umer, T., & Kim, B.-S. (2022) · Electronics · doi:10.3390/electronics11040670 · Cited by 157 · see citing works
“[81] 2019 Mobile edge computing FL NA DRL techniques are integrated with FL framework to optimize the MEC communication and caching.”
BackgroundMethodEdge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
Yao, J., Zhang, S., Yao, Y., Wang, F., Ma, J., Zhang, J., Chu, Y., Ji, L., Jia, K., Shen, T., et al. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3178211 · Cited by 156 · see citing works
“Given the above constraints, edge computing polarizes AI to a new research era, namely edge intelligence or edge AI(1)[15], [16], [17].”
BackgroundShuja, J., Bilal, K., Alasmary, W., Sinky, H., & Alanazi, E. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103005 · Cited by 157 · see citing works
“udies model some parameters of the optimization problem as MDP. For example, reference [29] model user content request as Markov process with unknown transition probabilities, references [158], [52], [97] model the cache replacement problem as MDP, in reference [46] mobility patterns of users are characterized as Mark…”
BackgroundMethodInfluentialWang, X., Li, R., Wang, C., Li, X., Taleb, T., & Leung, V. C. M. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3036946 · Cited by 152 · see citing works
Self-citationSalh, A., Audah, L., Shah, N. S. M., Alhammadi, A., Abdullah, Q., Kim, Y. H., Al-Gailani, S. A., Hamzah, S. A., Esmail, B. A. F., & Almohammedi, A. A. (2021) · IEEE Access · doi:10.1109/access.2021.3069707 · Cited by 152 · see citing works
“The proposed ML training entails controller switches, multiple training processors at the cell level of edge intelligence to enhance the accounting of transmission overhead and the channel dynamics [30], [161], [237], [238].”
BackgroundChallenges, Applications and Design Aspects of Federated Learning: A Survey
Rahman, K. M. J., Ahmed, F., Akhter, N., Hasan, M., Amin, R., Aziz, K. E., Islam, A. K. M. M., Mukta, M. S. H., & Islam, A. K. M. N. (2021) · IEEE Access · doi:10.1109/access.2021.3111118 · Cited by 147 · see citing works
“Applications of FL in mobile devices included predicting user input [74], [86], [87], emoji [88], human trajectory [89], and human behavior [90]; reducing network congestion [91]; detecting physical hazards (smart-home IoT) [92]; performing industrial engineering—environmental”
BackgroundOffloading Time Optimization via Markov Decision Process in Mobile-Edge Computing
Yang, G., Hou, L., He, X., He, D., Chan, S., & Guizani, M. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3033285 · Cited by 145 · see citing works
“As a new architecture, mobile-edge computing (MEC) is introduced to bridge the drawback of the remote cloud by the network edges [4], which can shorten the propagation delay and enhance the computational capability of mobile devices.”
Background
- 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 · 2018: 2
- 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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