The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Fast-Convergent Federated Learning
Open accessAbstract
Federated learning has emerged recently as a promising solution for distributing machine learning tasks through modern networks of mobile devices. Recent studies have obtained lower bounds on the expected decrease in model loss that is achieved through each round of federated learning. However, convergence generally requires a large number of communication rounds, which induces delay in model training and is costly in terms of network resources. In this paper, we propose a fast-convergent federated learning algorithm, called$\mathsf {FOLB}$, which performs intelligent sampling of devices in each round of model training to optimize the expected convergence speed. We first theoretically characterize a lower bound on improvement that can be obtained in each round if devices are selected according to the expected improvement their local models will provide to the current global model. Then, we show that$\mathsf {FOLB}$obtains this bound through uniform sampling by weighting device updates according to their gradient information.$\mathsf {FOLB}$is able to handle both communication and computation heterogeneity of devices by adapting the aggregations according to estimates of device’s capabilities of contributing to the updates. We evaluate$\mathsf {FOLB}$in comparison with existing federated learning algorithms and experimentally show its improvement in trained model accuracy, convergence speed, and/or model stability across various machine learning tasks and datasets.
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Cites
Gradient-based learning applied to document recognition
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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
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210
Federated Optimization in Heterogeneous Networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2018) · arXiv · doi:10.48550/arxiv.1812.06127 · Cited by 4,023
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., & Suresh, A. T. (2019) · arXiv · doi:10.48550/arxiv.1910.06378 · Cited by 1,734
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. (2019) · arXiv · doi:10.48550/arxiv.1909.07972 · Cited by 1,547
Self-citationA 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
Self-citationConvergence of Edge Computing and Deep Learning: A Comprehensive Survey
Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498 · cited 1×
“Other aspects of federated learning have also been studied, such as privacy of user data [27]–[31], fairness in federated learning [32], federated learning over communication systems [33]–[37], and federated learning for edge networks [38]–[40].”
BackgroundAdaptive Federated Optimization
Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., & McMahan, H. B. (2020) · arXiv · doi:10.48550/arxiv.2003.00295 · Cited by 840
Cited by
Model aggregation techniques in federated learning: A comprehensive survey
Qi, P., Chiaro, D., Guzzo, A., Ianni, M., Fortino, G., & Piccialli, F. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2023.09.008 · Cited by 452
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling
Luo, B., Xiao, W., Wang, S., Huang, J., & Tassiulas, L. (2022) · IEEE INFOCOM 2022 - IEEE Conference on Computer Communications · doi:10.1109/infocom48880.2022.9796935 · Cited by 229
Yaqoob, M. M., Nazir, M., Khan, M. A., Qureshi, S., & Al-Rasheed, A. (2023) · Applied Sciences · doi:10.3390/app13031911 · Cited by 96
Li, L., Yang, L., Guo, X., Shi, Y., Wang, H., Chen, W., & Letaief, K. B. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3118431 · Cited by 37
Metadata
- Authors
- Nguyen, H. T., Sehwag, V., Hosseinalipour, S., Brinton, C. G., Chiang, M., & Vincent Poor, H.
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 2021-01
- Volume
- 39
- Issue
- 1
- Pages
- 201-218
- ISSN
- 0733-8716 (ISSN-L)
- 1558-0008
- OpenAlex
- W3044765474
- Cited by
- 277
- References
- 15
- FWCI
- 12.22
- Type
- journal article
- Language
- en
- Open access
- green
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