Towards Flexible Device Participation in Federated Learning

Yichen Ruan, Xiaoxi Zhang, Shu-Che Liang, Carlee Joe-Wong
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:3403-3411, 2021.

Abstract

Traditional federated learning algorithms impose strict requirements on the participation rates of devices, which limit the potential reach of federated learning. This paper extends the current learning paradigm to include devices that may become inactive, compute incomplete updates, and depart or arrive in the middle of training. We derive analytical results to illustrate how allowing more flexible device participation can affect the learning convergence when data is not independently and identically distributed (non-IID). We then propose a new federated aggregation scheme that converges even when devices may be inactive or return incomplete updates. We also study how the learning process can adapt to early departures or late arrivals, and analyze their impacts on the convergence.

Cite this Paper

BibTeX
@InProceedings{pmlr-v130-ruan21a, title = { Towards Flexible Device Participation in Federated Learning }, author = {Ruan, Yichen and Zhang, Xiaoxi and Liang, Shu-Che and Joe-Wong, Carlee}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {3403--3411}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/ruan21a/ruan21a.pdf}, url = {https://proceedings.mlr.press/v130/ruan21a.html}, abstract = { Traditional federated learning algorithms impose strict requirements on the participation rates of devices, which limit the potential reach of federated learning. This paper extends the current learning paradigm to include devices that may become inactive, compute incomplete updates, and depart or arrive in the middle of training. We derive analytical results to illustrate how allowing more flexible device participation can affect the learning convergence when data is not independently and identically distributed (non-IID). We then propose a new federated aggregation scheme that converges even when devices may be inactive or return incomplete updates. We also study how the learning process can adapt to early departures or late arrivals, and analyze their impacts on the convergence. } }
Endnote
%0 Conference Paper %T Towards Flexible Device Participation in Federated Learning %A Yichen Ruan %A Xiaoxi Zhang %A Shu-Che Liang %A Carlee Joe-Wong %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-ruan21a %I PMLR %P 3403--3411 %U https://proceedings.mlr.press/v130/ruan21a.html %V 130 %X Traditional federated learning algorithms impose strict requirements on the participation rates of devices, which limit the potential reach of federated learning. This paper extends the current learning paradigm to include devices that may become inactive, compute incomplete updates, and depart or arrive in the middle of training. We derive analytical results to illustrate how allowing more flexible device participation can affect the learning convergence when data is not independently and identically distributed (non-IID). We then propose a new federated aggregation scheme that converges even when devices may be inactive or return incomplete updates. We also study how the learning process can adapt to early departures or late arrivals, and analyze their impacts on the convergence.
APA
Ruan, Y., Zhang, X., Liang, S. & Joe-Wong, C.. (2021). Towards Flexible Device Participation in Federated Learning . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:3403-3411 Available from https://proceedings.mlr.press/v130/ruan21a.html.

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