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19 April 2024 |
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Federated Learning with Fair Averaging | | Zheng Wang
; Xiaoliang Fan
; Jianzhong Qi
; Chenglu Wen
; Cheng Wang
; Rongshan Yu
; | | Date: |
30 Apr 2021 | | Abstract: | Fairness has emerged as a critical problem in federated learning (FL). In
this work, we identify a cause of unfairness in FL -- emph{conflicting}
gradients with large differences in the magnitudes. To address this issue, we
propose the federated fair averaging (FedFV) algorithm to mitigate potential
conflicts among clients before averaging their gradients. We first use the
cosine similarity to detect gradient conflicts, and then iteratively eliminate
such conflicts by modifying both the direction and the magnitude of the
gradients. We further show the theoretical foundation of FedFV to mitigate the
issue conflicting gradients and converge to Pareto stationary solutions.
Extensive experiments on a suite of federated datasets confirm that FedFV
compares favorably against state-of-the-art methods in terms of fairness,
accuracy and efficiency. | | Source: | arXiv, 2104.14937 | | Services: | Forum | Review | PDF | Favorites | |
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