Distribution-Regularized Federated Learning on Non-IID Data
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | Proceedings - 2023 IEEE 39th International Conference on Data Engineering (ICDE 2023) |
Publisher | Institute of Electrical and Electronics Engineers, Inc. |
Pages | 2113-2125 |
ISBN (electronic) | 979-8-3503-2227-9 |
Publication status | Published - 2023 |
Publication series
Name | Proceedings - International Conference on Data Engineering |
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Volume | 2023-April |
ISSN (Print) | 1084-4627 |
Conference
Title | 39th IEEE International Conference on Data Engineering (ICDE 2023) |
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Location | Marriott Anaheim |
Place | United States |
City | Anaheim |
Period | 3 - 7 April 2023 |
Link(s)
Abstract
Federated learning (FL) has emerged as a popular machine learning paradigm recently. Compared with traditional distributed learning, its unique challenges mainly lie in communication efficiency and non-IID (heterogeneous data) problem. While the widely adopted framework FedAvg can reduce communication overhead significantly, its effectiveness on non-IID data still lacks exploration. In this paper, we study the non-IID problem of FL from the perspective of domain adaptation. We propose a distribution regularization for FL on non-IID data such that the discrepancy of data distributions between clients is reduced. To further reduce the communication cost, we devise two novel distributed learning algorithms, namely rFedAvg and rFedAvg+, for efficiently learning with the distribution regularization. More importantly, we theoretically establish their convergence for strongly convex objectives. Extensive experiments on 4 datasets with both CNN and LSTM as learning models verify the effectiveness and efficiency of the proposed algorithms. © 2023 IEEE.
Citation Format(s)
Distribution-Regularized Federated Learning on Non-IID Data. / Wang, Yansheng; Tong, Yongxin; Zhou, Zimu et al.
Proceedings - 2023 IEEE 39th International Conference on Data Engineering (ICDE 2023). Institute of Electrical and Electronics Engineers, Inc., 2023. p. 2113-2125 (Proceedings - International Conference on Data Engineering; Vol. 2023-April).
Proceedings - 2023 IEEE 39th International Conference on Data Engineering (ICDE 2023). Institute of Electrical and Electronics Engineers, Inc., 2023. p. 2113-2125 (Proceedings - International Conference on Data Engineering; Vol. 2023-April).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review