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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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Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
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
Towards Federated Learning at Scale: System Design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, H. B., et al. (2019) · arXiv · doi:10.48550/arxiv.1902.01046 · Cited by 2,334
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
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
Nishio, T., & Yonetani, R. (2019) · ICC 2019 - 2019 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2019.8761315 · Cited by 1,875
On the Convergence of FedAvg on Non-IID Data
Li, X., Huang, K., Yang, W., Wang, S., & Zhang, Z. (2019) · arXiv · doi:10.48550/arxiv.1907.02189 · Cited by 1,575
A 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
Data Security and Privacy Protection Issues in Cloud Computing
Chen, D., & Zhao, H. (2012) · International Conference on Computer Science and Electronics Engineering (ICCSEE) · doi:10.1109/iccsee.2012.193 · Cited by 830
Speeding Up Distributed Machine Learning Using Codes
Lee, K., Lam, M., Pedarsani, R., Papailiopoulos, D., & Ramchandran, K. (2018) · IEEE Transactions on Information Theory · doi:10.1109/tit.2017.2736066 · Cited by 829
Broadband Analog Aggregation for Low-Latency Federated Edge Learning
Zhu, G., Wang, Y., & Huang, K. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2946245 · Cited by 817
Research works printed but not linked: 4
- 10.1109/GLOCOM.2018.8647927 · DOI · Paper page
- 10.1109/JPROC.2019.2941458 · DOI · Paper page
- goodfellow, 2016, Deep Learning, 1
- 0, Forecast and Methodology
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Zhou, S., Feng, L., Mei, M., & Yao, M.-W. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3414472 · Cited by 12
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Yan, Q., Wang, G., Zhu, D., & Li, J. (2025) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001425520135 · Cited by 1
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Applications in AIoT: Federated Distributed Learning for Edge IoT
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FedProf: Efficient Federated Learning with Data Representation ProfilingPreprint
Wu, W., He, L., Lin, W., & Mao, R. (2021) · arXiv (Cornell University) · Cited by 1
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Zheng, S., Shen, C., & Chen, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.04057
Jin, R. (2020) · NCSU Libraries Repository (North Carolina State University Libraries)
Albaseer, A. M., Abdallah, M. M., Al‐Fuqaha, A., & Erbad, A. (2022) · doi:10.36227/techrxiv.19697293
Albaseer, A. M., Abdallah, M. M., Al‐Fuqaha, A., & Erbad, A. (2022) · doi:10.36227/techrxiv.19697293.v1
Endogenous Security-Aware Device Scheduling for Federated Learning-Assisted Low-Carbon Smart Park
Yao, Z., Zhang, S., Yu, Z., Zhou, Z., Mumtaz, S., & Wang, X. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10279197
Latency Minimization for Federated Learning over Wireless Networks under Energy Constraint
Chen, B., Huang, H., & Hu, Y. (2023) · International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT) · doi:10.1109/ccpqt60491.2023.00050
Pan, J., Chen, Z., Zhao, M., Zhang, S., & Zhu, J. (2023) · International Conference on Future Communications and Networks (FCN) · doi:10.1109/fcn60432.2023.10543564
Metadata
- 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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