Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT
Abstract
The rapidly expanding number of Internet of Things (IoT) devices is generating huge quantities of data, but public concern over data privacy means users are apprehensive to send data to a central server for machine learning (ML) purposes. The easily changed behaviors of edge infrastructure that software-defined networking (SDN) provides makes it possible to collate IoT data at edge servers and gateways, where federated learning (FL) can be performed: building a central model without uploading data to the server. FedAvg is an FL algorithm which has been the subject of much study, however, it suffers from a large number of rounds to convergence with non-independent identically distributed (non-IID) client data sets and high communication costs per round. We propose adapting FedAvg to use a distributed form of Adam optimization, greatly reducing the number of rounds to convergence, along with the novel compression techniques, to produce communication-efficient FedAvg (CE-FedAvg). We perform extensive experiments with the MNIST/CIFAR-10 data sets, IID/non-IID client data, varying numbers of clients, client participation rates, and compression rates. These show that CE-FedAvg can converge to a target accuracy in up to 6× less rounds than similarly compressed FedAvg, while uploading up to 3× less data, and is more robust to aggressive compression. Experiments on an edge-computing-like testbed using Raspberry Pi clients also show that CE-FedAvg is able to reach a target accuracy in up to 1.7× less real time than FedAvg.
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Implementation results on an industrial edge computing-like testbed using Raspberry Pi clients show the effectiveness of CE-FedAvg with lower communication latency while training accuracy is preserved.
“Implementation results on an industrial edge computing-like testbed using Raspberry Pi clients show the effectiveness of the proposed scheme with lower communication latency while the training accuracy is preserved.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [174]
To enable intelligent and privacy-enhanced edge-based industrial IoT applications, many efficient FL solutions have been introduced to solve communication issues by using update reconstruction techniques or relevance detection during the model update.
“Furthermore, to enable intelligent and privacy-enhanced edgebased industrial IoT applications, many efficient FL solutions have been introduced to solve communication issues by using update reconstruction techniques [174], [175] or relevance detection during the model update [177].”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [174]
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,890 · see citing works
(2010) · Cited by 8,261 · see citing works
Federated Learning: Strategies for Improving Communication Efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766 · see citing works
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., & Chan, K. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904348 · Cited by 2,232 · see citing works
Federated Learning with Non-IID Data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077 · see citing works
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data
Sattler, F., Wiedemann, S., Muller, K.-R., & Samek, W. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2944481 · Cited by 1,725 · see citing works
Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing
Li, H., Ota, K., & Dong, M. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700202 · Cited by 1,566 · see citing works
Federated Learning for Mobile Keyboard Prediction
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., & Ramage, D. (2018) · arXiv · doi:10.48550/arxiv.1811.03604 · Cited by 1,148 · see citing works
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Lin, Y., Han, S., Mao, H., Wang, Y., & Dally, W. J. (2017) · arXiv · doi:10.48550/arxiv.1712.01887 · Cited by 1,134 · see citing works
Run-length encodings (Corresp.)
Golomb, S. W. (1966) · IEEE Transactions on Information Theory · doi:10.1109/tit.1966.1053907 · Cited by 990 · see citing works
Federated Learning of Deep Networks using Model AveragingPreprint
McMahan, H. B., Moore, E., Ramage, D., & y Arcas, B. A. (2016) · arXiv (Cornell University) · Cited by 923 · see citing works
Chen, Y., Sun, X., & Jin, Y. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2953131 · Cited by 575 · see citing works
Federated Learning: Collaborative Machine Learning without Centralized Training Data
Abhishek, V. A., Binny, S., Johan, T. R., Raj, N., & Thomas, V. (2022) · International Journal of Engineering Technology and Management Sciences · doi:10.46647/ijetms.2022.v06i05.052 · Cited by 489 · see citing works
Liu, C., Cao, Y., Luo, Y., Chen, G., Vokkarane, V., Yunsheng, M., Chen, S., & Hou, P. (2018) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2017.2662008 · Cited by 333 · see citing works
Federated Learning for Keyword Spotting
Leroy, D., Coucke, A., Lavril, T., Gisselbrecht, T., & Dureau, J. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683546 · Cited by 291 · see citing works
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication
Sattler, F., Wiedemann, S., Muller, K.-R., & Samek, W. (2019) · 2019 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852172 · Cited by 240 · see citing works
Federated Learning for Emoji Prediction in a Mobile Keyboard
Ramaswamy, S., Mathews, R., Rao, K., & Beaufays, F. (2019) · arXiv · doi:10.48550/arxiv.1906.04329 · Cited by 231 · see citing works
Pathinarupothi, R. K., Durga, P., & Rangan, E. S. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2870068 · Cited by 220 · see citing works
Yao, S., Zhao, Y., Zhang, A., Su, L., & Abdelzaher, T. (2017) · Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems · doi:10.1145/3131672.3131675 · Cited by 188 · see citing works
Multi-subpopulation evolutionary algorithms for coverage deployment of UAV-networks
Reina, D. G., Tawfik, H., & Toral, S. L. (2017) · Ad Hoc Networks · doi:10.1016/j.adhoc.2017.09.005 · Cited by 76 · see citing works
Fog-Embedded Deep Learning for the Internet of Things
Lyu, L., Bezdek, J. C., He, X., & Jin, J. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2912465 · Cited by 61 · see citing works
Edge-Assisted Hierarchical Federated Learning with Non-IID DataPreprint
Liu, L., Zhang, J., Song, S., & Letaief, K. B. (2019) · arXiv (Cornell University) · Cited by 47 · see citing works
A New Learning Automata-Based Pruning Method to Train Deep Neural Networks
Guo, H., Li, S., Li, B., Ma, Y., & Ren, X. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2711426 · Cited by 40 · see citing works
Strategies for Re-Training a Pruned Neural Network in an Edge Computing Paradigm
Chandakkar, P. S., Li, Y., Ding, P. L. K., & Li, B. (2017) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/ieee.edge.2017.45 · Cited by 39 · see citing works
Flagged first, then by citations
2 of the 27 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Federated 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
“[174] Communicationefficient FL HFL NN Edge devices Cloud server A communication-efficient FL approach called CE-FedAvg for optimizing communication rounds.”
BackgroundMethodFederated learning on non-IID data: A survey
Zhu, H., Xu, J., Liu, S., & Jin, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.098 · Cited by 1,062 · see citing works
Federated 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
Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research
de Alwis, C., Kalla, A., Pham, Q., Kumar, P., Dev, K., Hwang, W., & Liyanage, M. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3071496 · Cited by 809 · see citing works
Federated 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
A Learning-Based Incentive Mechanism for Federated Learning
Zhan, Y., Li, P., Qu, Z., Zeng, D., & Guo, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2967772 · Cited by 602 · see citing works
Privacy-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
Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things
Zhou, X., Liang, W., Wang, K. I., Wang, H. H., Yang, L. T., & Jin, Q. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2985082 · Cited by 557 · 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
Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey
Duan, S., Wang, D., Ren, J., Lyu, F., Zhang, Y., Wu, H., & Shen, X. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3218527 · Cited by 419 · see citing works
A 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
Federated 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
Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning
Zhang, L., Shen, L., Ding, L., Tao, D., & Duan, L. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00993 · Cited by 391 · see citing works
Dynamic-Fusion-Based Federated Learning for COVID-19 Detection
Zhang, W., Zhou, T., Lu, Q., Wang, X., Zhu, C., Sun, H., Wang, Z., Lo, S. K., & Wang, F.-Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3056185 · Cited by 329 · see citing works
Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing
Wang, Y., Su, Z., Zhang, N., & Benslimane, A. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.3014385 · Cited by 323 · see citing works
FedMCCS: Multicriteria Client Selection Model for Optimal IoT Federated Learning
AbdulRahman, S., Tout, H., Mourad, A., & Talhi, C. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3028742 · Cited by 323 · see citing works
FELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things
Friha, O., Ferrag, M. A., Shu, L., Maglaras, L. A., Choo, K. R., & Nafa, M. (2022) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2022.03.003 · Cited by 319 · see citing works
A Review of Federated Learning Methods in Heterogeneous Scenarios
Pei, J., Liu, W., Li, J., Wang, L., & Liu, C. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3385440 · 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
Wang, X., Garg, S., Lin, H., Hu, J. W., Kaddoum, G., Piran, M. J., & Hossain, M. S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3074382 · Cited by 269 · see citing works
Self-citationBaccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A. M. S., Hamdi, M., & Guizani, M. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3200740 · Cited by 209 · see citing works
Yang, W., Xiang, W., Yang, Y. W., & Cheng, P. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3183465 · Cited by 192 · see citing works
Hu, S., Chen, X., Ni, W., Hossain, E., & Wang, X. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3086014 · Cited by 178 · see citing works
Lo, S. K., Lu, Q., Wang, C., Paik, H.-Y., & Zhu, L. (2022) · ACM Computing Surveys · doi:10.1145/3450288 · Cited by 202 · see citing works
Duan, Q., Huang, J., Hu, S., Deng, R., Lu, Z., & Yu, S. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3316615 · Cited by 175 · see citing works
Federated Learning Over Wireless IoT Networks With Optimized Communication and Resources
Chen, H., Huang, S., Zhang, D., Xiao, M., Skoglund, M., & Poor, H. V. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3151193 · Cited by 156 · 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
Federated learning in smart cities: Privacy and security survey
Al-Huthaifi, R., Li, T., Huang, W. X., Gu, J., & Li, C. (2023) · Information Sciences · doi:10.1016/j.ins.2023.03.033 · Cited by 160 · see citing works
FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things
Yin, B., Yin, H., Wu, Y., & Jiang, Z. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2966778 · Cited by 149 · see citing works
Chai, Z., Chen, Y., Anwar, A., Zhao, L., Cheng, Y., & Rangwala, H. (2021) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3458817.3476211 · Cited by 143 · see citing works
Asynchronous Federated Learning Over Wireless Communication Networks
Wang, Z., Zhang, Z., Tian, Y., Yang, Q., Shan, H., Wang, W., & Quek, T. Q. S. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3153495 · Cited by 142 · see citing works
Efficient Federated Learning Algorithm for Resource Allocation in Wireless IoT Networks
Nguyen, V., Sharma, S. K., Vu, T. X., Chatzinotas, S., & Ottersten, B. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3022534 · Cited by 136 · see citing works
WSCC: A Weight-Similarity-Based Client Clustering Approach for Non-IID Federated Learning
Tian, P., Liao, W., Yu, W., & Blasch, E. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3175149 · Cited by 136 · see citing works
Decentralized Federated Learning via Mutual Knowledge Transfer
Li, C., Li, G., & Varshney, P. K. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3078543 · Cited by 134 · see citing works
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey
Wu, J., Dong, F., Leung, H., Zhu, Z., Zhou, J., & Drew, S. (2024) · ACM Computing Surveys · doi:10.1145/3659205 · Cited by 130 · see citing works
Guo, Q., Tang, F., & Kato, N. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3227655 · Cited by 128 · see citing works
FogFL: Fog-Assisted Federated Learning for Resource-Constrained IoT Devices
Saha, R., Misra, S., & Deb, P. K. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3046509 · Cited by 125 · see citing works
Federated Learning for Computationally Constrained Heterogeneous Devices: A Survey
Pfeiffer, K., Rapp, M., Khalili, R., & Henkel, J. (2023) · ACM Computing Surveys · doi:10.1145/3596907 · Cited by 125 · see citing works
Communication-Efficient Federated Learning Based on Compressed Sensing
Li, C., Li, G., & Varshney, P. K. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3073112 · Cited by 116 · see citing works
Xu, W., Fang, W., Ding, Y., Zou, M., & Xiong, N. (2021) · IEEE Access · doi:10.1109/access.2021.3063291 · Cited by 112 · see citing works
Rahman, A., Hasan, K., Kundu, D., Islam, M. J., Debnath, T., Band, S. S., & Kumar, N. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.08.004 · Cited by 111 · see citing works
Architectural patterns for the design of federated learning systems
Lo, S. K., Lu, Q., Zhu, L., Paik, H.-Y., Xu, X., & Wang, C. (2022) · Journal of Systems and Software · doi:10.1016/j.jss.2022.111357 · Cited by 109 · see citing works
Decentralised Learning in Federated Deployment Environments
Bellavista, P., Foschini, L., & Mora, A. (2021) · ACM Computing Surveys · doi:10.1145/3429252 · Cited by 101 · see citing works
Trust Management for Internet of Things: A Comprehensive Study
Wei, L., Yang, Y., Wu, J. B., Long, C., & Li, B. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3139989 · Cited by 104 · see citing works
Liu, Y., Jiang, L., Qi, Q., & Xie, S. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3220677 · Cited by 98 · see citing works
Malik, H., Naeem, A., Naqvi, R. A., & Loh, W.-K. (2023) · Sensors · doi:10.3390/s23020743 · Cited by 97 · see citing works
Auction-Based Cluster Federated Learning in Mobile Edge Computing Systems
Lu, R., Zhang, W., Wang, Y., Li, Q., Zhong, X., Yang, H., & Wang, D. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3240767 · Cited by 97 · see citing works
Tariq, A., Serhani, M. A., Sallabi, F., Barka, E. S., Qayyum, T., Khater, H. M., & Shuaib, K. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3438264 · Cited by 110 · see citing works
Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning
Mohammed, I., Tabatabai, S., Al‐Fuqaha, A., Bouanani, F. E., Qadir, J., Qolomany, B., & Guizani, M. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3036157 · Cited by 89 · see citing works
Zhang, T., Li, Y., & Chen, C. L. P. (2020) · Information Sciences · doi:10.1016/j.ins.2020.12.021 · Cited by 92 · see citing works
Efficient Federated Learning for Cloud-Based AIoT Applications
Zhang, X., Hu, M., Xia, J., Wei, T., Chen, M., & Hu, S. (2020) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2020.3046665 · Cited by 87 · see citing works
Joint Training and Resource Allocation Optimization for Federated Learning in UAV Swarm
Shen, Y. C., Qu, Y., Dong, C., Zhou, F., & Wu, Q. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3152829 · Cited by 86 · see citing works
Federated Learning and Its Role in the Privacy Preservation of IoT Devices
Alam, T., & Gupta, R. (2022) · Future Internet · doi:10.3390/fi14090246 · Cited by 84 · see citing works
Federated Learning in Robotic and Autonomous Systems
Xianjia, Y., Queralta, J. P., Heikkonen, J., & Westerlund, T. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.07.041 · Cited by 79 · see citing works
Securing massive IoT in 6G: Recent solutions, architectures, future directions
Alotaibi, A. I., & Barnawi, A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100715 · Cited by 81 · see citing works
A Review of Federated Learning in Agriculture
Žalik, K. R., & Žalik, M. (2023) · Sensors · doi:10.3390/s23239566 · Cited by 86 · see citing works
SHARE: Shaping Data Distribution at Edge for Communication-Efficient Hierarchical Federated Learning
Deng, Y., Lyu, F., Ren, J., Zhang, Y., Zhou, Y., Zhang, Y., & Yang, Y. (2021) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs51616.2021.00012 · Cited by 70 · see citing works
Joint Client Selection and Bandwidth Allocation Algorithm for Federated Learning
Ko, H., Lee, J., Seo, S., Pack, S., & Leung, V. C. M. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3136611 · Cited by 70 · see citing works
Xiao, Y. L., Sun, Z., Shi, G., & Niyato, D. T. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3229419 · Cited by 72 · see citing works
Neural network quantization in federated learning at the edge
Tonellotto, N., Gotta, A., Nardini, F. M., Gadler, D., & Silvestri, F. (2021) · Information Sciences · doi:10.1016/j.ins.2021.06.039 · Cited by 69 · see citing works
Federated Learning: Navigating the Landscape of Collaborative Intelligence
Lazaros, K., Koumadorakis, D. E., Vrahatis, A. G., & Kotsiantis, S. B. (2024) · Electronics · doi:10.3390/electronics13234744 · Cited by 66 · see citing works
Communication-efficient distributed AI strategies for the IoT edge
Mwase, C., Jin, Y., Westerlund, T., Tenhunen, H., & Zou, Z. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.01.013 · Cited by 64 · see citing works
Blika, A., Palmos, S., Doukas, G. S., Lamprou, V., Pelekis, S., Kontoulis, M., Ntanos, C., & Askounis, D. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3449563 · Cited by 69 · see citing works
FedTAR: Task and Resource-Aware Federated Learning for Wireless Computing Power Networks
Sun, W. Y., Li, Z., Wang, Q., & Zhang, Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3215805 · Cited by 61 · see citing works
Liu, W., Lin, H., Wang, X., Hu, J. W., Kaddoum, G., Piran, M. J., & Alamri, A. M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3081606 · Cited by 57 · see citing works
Self-citationAsynchronous Federated Learning for Sensor Data with Concept Drift
Chen, Y., Chai, Z., Cheng, Y., & Rangwala, H. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671924 · Cited by 57 · see citing works
Enabling All In-Edge Deep Learning: A Literature Review
Joshi, P., Hasanuzzaman, M., Thapa, C., Afli, H., & Scully, T. (2023) · IEEE Access · doi:10.1109/access.2023.3234761 · Cited by 60 · see citing works
SPIDER: A Social Computing Inspired Predictive Routing Scheme for Softwarized Vehicular Networks
Zhao, L., Zheng, T., Lin, M., Hawbani, A., Shang, J., & Fan, C. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3122438 · Cited by 55 · see citing works
Lyapunov-Based Optimization of Edge Resources for Energy-Efficient Adaptive Federated Learning
Battiloro, C., Lorenzo, P. D., Merluzzi, M., & Barbarossa, S. (2022) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2022.3186879 · Cited by 54 · see citing works
Partial Synchronization to Accelerate Federated Learning Over Relay-Assisted Edge Networks
Qu, Z., Guo, S., Wang, H., Ye, B., Wang, Y., Zomaya, A. Y., & Tang, B. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3083154 · Cited by 54 · see citing works
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data
Sun, Y., Shao, J., Mao, Y., Wang, J. H., & Zhang, J. (2022) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc51071.2022.9771904 · Cited by 53 · see citing works
Toward Secure Data Fusion in Industrial IoT Using Transfer Learning
Lin, H., Hu, J., Wang, X., Alhamid, M. F., & Piran, M. J. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3038780 · Cited by 53 · see citing works
Self-citationFederated learning design and functional models: survey
Ayeelyan, J., Utomo, S., Rouniyar, A., Hsu, H.-C., & Hsiung, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10969-y · Cited by 56 · see citing works
Federated learning for intrusion detection in IoT environments: a privacy-preserving strategy
Khraisat, A., Alazab, A., Alazab, M., Obeidat, A., Singh, S., & Jan, T. (2025) · Discover Internet of Things · doi:10.1007/s43926-025-00169-7 · Cited by 51 · see citing works
Ji, X., Tian, J., Zhang, H.-X., Wu, D.-L., & Li, T.-T. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3233595 · Cited by 50 · see citing works
Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey
Asad, M., Shaukat, S., Hu, D., Wang, Z., Javanmardi, E., Nakazato, J., & Tsukada, M. (2023) · Sensors · doi:10.3390/s23177358 · Cited by 55 · see citing works
Xu, Z.-C., Zhao, D., Liang, W.-F., Rana, O., Zhou, P., Li, M.-C., Xu, W., Li, H., & Xia, Q.-F. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3218807 · Cited by 49 · see citing works
Federated deep reinforcement learning based secure data sharing for Internet of Things
Miao, Q., Lin, H., Wang, X., & Hassan, M. M. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108327 · Cited by 48 · see citing works
Applying Federated Learning in Software-Defined Networks: A Survey
Ma, X., Liao, L., Li, Z., Lai, X.-R., & Zhang, M. (2022) · Symmetry · doi:10.3390/sym14020195 · Cited by 39 · see citing works
Blockchain-empowered secure federated learning system: Architecture and applications
Yu, F., Lin, H. D., Wang, X., Yassine, A., & Hossain, M. S. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.09.008 · Cited by 47 · see citing works
Adam, M., & Baroudi, U. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3506214 · Cited by 46 · see citing works
Zhang, Y. (2021) · Simula SpringerBriefs on computing · doi:10.1007/978-3-030-83944-4 · Cited by 46 · see citing works
Distance-Aware Hierarchical Federated Learning in Blockchain-Enabled Edge Computing Network
Huang, X., Wu, Y., Liang, C., Chen, Q., & Zhang, J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3279983 · Cited by 46 · see citing works
Unlocking a Promising Future: Integrating Blockchain Technology and FL-IoT in the Journey to 6G
Alghamedy, F. H., El-Haggar, N., Alsumayt, A., Alfawaer, Z. M., Alshammari, M., Amouri, L., Aljameel, S. S., & Albassam, S. (2024) · IEEE Access · doi:10.1109/access.2024.3435968 · Cited by 46 · see citing works
FedDQ: Communication-Efficient Federated Learning with Descending Quantization
Qu, L., Song, S., & Tsui, C.-Y. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001205 · Cited by 44 · see citing works
Nguyen, M.-D., Lee, S., Pham, Q., Hoang, D. T., Nguyen, D. N., & Hwang, W. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3190510 · Cited by 43 · see citing works
Data Poisoning in Sequential and Parallel Federated Learning
Nuding, F., & Mayer, R. (2022) · Proceedings of the 2022 ACM on International Workshop on Security and Privacy Analytics · doi:10.1145/3510548.3519372 · Cited by 42 · see citing works
Zhang, Z., Zhang, Y., Guo, D., Zhao, S., & Zhu, X. (2022) · Science China Information Sciences · doi:10.1007/s11432-020-3419-4 · Cited by 42 · see citing works
Alotaibi, A. I., & Barnawi, A. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101575 · Cited by 42 · see citing works
Deep Actor–Critic Learning-Based Robustness Enhancement of Internet of Things
Chen, N., Qiu, T., Mu, C., Han, M., & Zhou, P. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2963499 · Cited by 42 · see citing works
Semi-Decentralized Federated Edge Learning With Data and Device Heterogeneity
Sun, Y., Shao, J., Mao, Y., Wang, J. H., & Zhang, J. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3252818 · Cited by 41 · see citing works
Attention-based federated incremental learning for traffic classification in the Internet of Things
Zhu, M., Chen, Z., Chen, K., Lv, N., & Zhong, Y. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.01.006 · Cited by 40 · see citing works
Collaborative Machine Learning: Schemes, Robustness, and Privacy
Wang, J., Pal, A., Yang, Q., Kant, K., Zhu, K., & Guo, S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3169347 · Cited by 42 · see citing works
Xu, Z., Li, D., Liang, W., Xu, W., Xia, Q., Zhou, P., RANA, O. F., & Li, H. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3262829 · Cited by 40 · see citing works
Communication-Efficient Federated Learning on Non-IID Data Using Two-Step Knowledge Distillation
Wen, H., Wu, Y., Hu, J. W., Wang, Z., Duan, H., & Min, G. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3276865 · Cited by 40 · see citing works
Self-citationRachakonda, S., Moorthy, S., Jain, A., Bukharev, A., Bucur, A., Manni, F., Quiterio, T. M., Joosten, L., & Mendez, N. I. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3185418 · Cited by 39 · see citing works
Computation Offloading and Resource Allocation Based on DT-MEC-Assisted Federated Learning Framework
He, Y., Yang, M., He, Z., & Guizani, M. (2023) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2023.3298926 · Cited by 39 · see citing works
A Comprehensive Overview of IoT-Based Federated Learning: Focusing on Client Selection Methods
Khajehali, N., Yan, J., Chow, Y.-W., & Fahmideh, M. (2023) · Italian National Conference on Sensors · doi:10.3390/s23167235 · Cited by 37 · see citing works
CASIT: Collective Intelligent Agent System for Internet of Things
Zhong, N., Wang, Y., Xiong, R., Zheng, Y., Li, Y., Ouyang, M., Shen, D., & Zhu, X. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3366906 · Cited by 38 · see citing works
Reputation-Aware Federated Learning Client Selection Based on Stochastic Integer Programming
Tan, X., Ng, W. C., Lim, W. Y. B., Xiong, Z., Niyato, D. T., & Yu, H. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3191332 · Cited by 37 · see citing works
- Authors
- Mills, J., Hu, J., & Min, G.
- Affiliations
- University of Exeter (GB)
- Venue
- IEEE Internet of Things Journal
- Published
- 2020-07
- Volume
- 7
- Issue
- 7
- Pages
- 5986-5994
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W2991236681
- Cited by
- 436
- References
- 27
- Citations by year
- 2026: 28 · 2025: 50 · 2024: 88 · 2023: 89 · 2022: 96 · 2021: 56 · 2020: 29
- FWCI
- 20.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Open Research Exeter (University of Exeter) (acceptedVersion)
- Figshare (submittedVersion)
- Funders
- EPSRC DTP Studentship
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- federated learning, communication efficiency, Internet of Things, FedAvg, data compression, non-IID data, edge computing, data privacy, software-defined networking
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