Group Fair Federated Learning via Stochastic Kernel Regularization
Abstract: Ensuring \textbf{group fairness} in federated learning (FL) presents unique challenges due to data heterogeneity and communication constraints. We propose Kernel Fair Federated Learning (\texttt{KFFL}), a novel framework that incorporates group fairness into FL models using the Kernel Hilbert-Schmidt Independence Criterion (KHSIC) as a fairness regularizer. To address scalability, \texttt{KFFL} approximates KHSIC with Random Feature Maps (RFMs), significantly reducing computational and communication overhead while achieving \textit{group fairness}.
To address the resulting non-convex optimization problem, we propose \texttt{FedProxGrad}, a federated proximal gradient algorithm that guarantees convergence. Through experiments on standard benchmark datasets across both IID and Non-IID settings for regression and classification tasks, \texttt{KFFL} demonstrates its ability to balance accuracy and fairness effectively, outperforming existing methods by comprehensively exploring the Pareto Frontier. Furthermore, we introduce \texttt{KFFL-TD}, a time-delayed variant that further reduces communication rounds, enhancing efficiency in decentralized environments.
Submission Length: Long submission (more than 12 pages of main content)
Assigned Action Editor: ~Han_Zhao1
Submission Number: 3833
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