FedFed: Feature Distillation against Data Heterogeneity in Federated Learning

• URL: http://arxiv.org/abs/2310.05077v1
• Date: Sun, 8 Oct 2023 09:00:59 GMT
• Title: FedFed: Feature Distillation against Data Heterogeneity in Federated Learning
• Authors: Zhiqin Yang, Yonggang Zhang, Yu Zheng, Xinmei Tian, Hao Peng, Tongliang Liu, Bo Han
• Abstract summary: Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. We propose a novel approach called textbfFederated textbfFeature textbfdistillation (FedFedFed) FedFed partitions data into performance-sensitive features (i.e., greatly contributing to model performance) and performance-robust features (i.e., limitedly contributing to model performance) Comprehensive experiments demonstrate the efficacy of FedFed in promoting model performance.
• Score: 88.36513907827552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data heterogeneity, yet incurs a dilemma in preserving privacy and promoting model performance. To alleviate the dilemma, we raise a fundamental question: \textit{Is it possible to share partial features in the data to tackle data heterogeneity?} In this work, we give an affirmative answer to this question by proposing a novel approach called {\textbf{Fed}erated \textbf{Fe}ature \textbf{d}istillation} (FedFed). Specifically, FedFed partitions data into performance-sensitive features (i.e., greatly contributing to model performance) and performance-robust features (i.e., limitedly contributing to model performance). The performance-sensitive features are globally shared to mitigate data heterogeneity, while the performance-robust features are kept locally. FedFed enables clients to train models over local and shared data. Comprehensive experiments demonstrate the efficacy of FedFed in promoting model performance.

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