Device Scheduling with Fast Convergence for Wireless Federated Learning
Open accessAbstract
Owing to the increasing need for massive data analysis and model training at the network edge, as well as the rising concerns about the data privacy, a new distributed training framework called federated learning (FL) has emerged. In each iteration of FL (called round), the edge devices update local models based on their own data and contribute to the global training by uploading the model updates via wireless channels. Due to the limited spectrum resources, only a portion of the devices can be scheduled in each round. While most of the existing work on scheduling focuses on the convergence of FL w.r.t. rounds, the convergence performance under a total training time budget is not yet explored. In this paper, a joint bandwidth allocation and scheduling problem is formulated to capture the long-term convergence performance of FL, and is solved by being decoupled into two sub-problems. For the bandwidth allocation sub-problem, the derived optimal solution suggests to allocate more bandwidth to the devices with worse channel conditions or weaker computation capabilities. For the device scheduling sub-problem, by revealing the trade-off between the number of rounds required to attain a certain model accuracy and the latency per round, a greedy policy is inspired, that continuously selects the device that consumes the least time in model updating until achieving a good trade-off between the learning efficiency and latency per round. The experiments show that the proposed policy outperforms other state-of-the-art scheduling policies, with the best achievable model accuracy under training time budgets.
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Mishra, K. N., Bhattacharjee, V., Saket, S., & Mishra, S. P. (2022) · Cluster Computing · doi:10.1007/s10586-022-03813-x · Cited by 25 · see citing works
Dap-FL: Federated Learning Flourishes by Adaptive Tuning and Secure Aggregation
Chen, Q., Wang, Z., Chen, J., Yan, H., & Lin, X. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3267897 · Cited by 25 · see citing works
Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation
Luo, B., Feng, Y., Wang, S., Huang, J., & Tassiulas, L. (2023) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs57875.2023.00027 · Cited by 25 · see citing works
Liu, W., Zhang, X., Duan, J., Joe‐Wong, C., Zhou, Z., & Chen, X. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3337016 · Cited by 24 · see citing works
FedPCC: Parallelism of Communication and Computation for Federated Learning in Wireless Networks
Zhang, H., Tian, H., Dong, M., Ota, K., & Jia, J. (2022) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3170471 · Cited by 23 · see citing works
Time Minimization in Hierarchical Federated Learning
Liu, C., Chua, T. J., & Zhao, J. (2022) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec54971.2022.00015 · Cited by 23 · see citing works
A Secure Personalized Federated Learning Algorithm for Autonomous Driving
Fu, Y., Tang, X., Li, C., Yu, F. R., & Cheng, N. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3450726 · Cited by 23 · see citing works
Towards Energy-Aware Federated Learning via Collaborative Computing Approach
Arouj, A., & Abdelmoniem, A. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.04.012 · Cited by 26 · see citing works
Reinforcement Learning-Based Device Scheduling for Renewable Energy-Powered Federated Learning
Cui, Y., Cao, K., & Wei, T. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3210008 · Cited by 21 · see citing works
Yu, T., Wang, X., Hu, J., & Yang, J. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3383860 · Cited by 22 · see citing works
DONE: Distributed Approximate Newton-type Method for Federated Edge Learning
Dinh, C. T., Tran, N. H. K., Nguyen, T. D., Bao, W., Balef, A. R., Zhou, B. B., & Zomaya, A. Y. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3146253 · Cited by 20 · see citing works
Deep reinforcement learning based scheduling strategy for federated learning in sensor-cloud systems
Zhang, T., Lam, K., & Zhao, J. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.03.009 · Cited by 20 · see citing works
FedProf: Selective Federated Learning based on Distributional Representation Profiling
Wu, W., He, L., Lin, W., & Maple, C. R. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3265588 · Cited by 23 · see citing works
Federated learning for minimizing nonsmooth convex loss functions
Wei, L.-Y., Yu, Z., & Zhou, D. (2023) · Mathematical Foundations of Computing · doi:10.3934/mfc.2023026 · Cited by 19 · see citing works
Le, T. H. T., Cantos, L., Pandey, S. R., Shin, H., & Kim, Y. H. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3264202 · Cited by 18 · see citing works
- Authors
- Shi, W., Zhou, S., & Niu, Z.
- Affiliations
- Beijing National Research Center for Information Science and Technology (CN)
- Venue
- ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 1-6
- ISSN
- 1550-3607 (ISSN-L)
- OpenAlex
- W3046056026
- Cited by
- 216
- References
- 16
- Citations by year
- 2026: 6 · 2025: 17 · 2024: 35 · 2023: 44 · 2022: 52 · 2021: 45 · 2020: 17
- FWCI
- 14.88
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Conference on Communications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Keywords
- wireless federated learning, device scheduling, bandwidth allocation, edge computing, model updating, channel conditions
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