Issue |
Wuhan Univ. J. Nat. Sci.
Volume 27, Number 6, December 2022
|
|
---|---|---|
Page(s) | 508 - 520 | |
DOI | https://doi.org/10.1051/wujns/2022276508 | |
Published online | 10 January 2023 |
CLC number: TP 301
MpFedcon : Model-Contrastive Personalized Federated Learning with the Class Center
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
Received:
24
August
2022
Federated learning is an emerging distributed privacy-preserving framework in which parties are trained collaboratively by sharing model or gradient updates instead of sharing private data. However, the heterogeneity of local data distribution poses a significant challenge. This paper focuses on the label distribution skew, where each party can only access a partial set of the whole class set. It makes global updates drift while aggregating these biased local models. In addition, many studies have shown that deep leakage from gradients endangers the reliability of federated learning. To address these challenges, this paper propose a new personalized federated learning method named MpFedcon. It addresses the data heterogeneity problem and privacy leakage problem from global and local perspectives. Our extensive experimental results demonstrate that MpFedcon yields effective resists on the label leakage problem and better performance on various image classification tasks, robust in partial participation settings, non-iid data, and heterogeneous parties.
Key words: personalized federated learning / layered network / model contrastive learning / gradient leakage
Biography: LI Xingchen, male, Master candidate, research direction: federated learning. E-mail: 351977119@qq.com
Supported by the Scientific and Technological Innovation 2030—Major Project of "New Generation Artificial Intelligence" (2020AAA 0109300)
© Wuhan University 2022
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