Robust Personalized Federated Learning with Sparse Penalization
提出一种个性化联邦学习方法，通过Huber损失和稀疏融合惩罚解决分布式数据下的鲁棒回归问题，并设计高效算法，理论证明收敛速度和统计一致性。
Federated learning (FL) is an emerging topic due to its advantage in collaborative learning with distributed data. Due to the heterogeneity in the local data-generating mechanism, it is important to consider personalization when developing federated learning methods. In this work, we propose a personalized federated learning (PFL) method to address the robust regression problem. Specifically, we aim to learn the regression weight by solving a Huber loss with the sparse fused penalty. Additionally, we designed our personalized federated learning for robust and sparse regression (PerFL-RSR) algorithm to solve the estimation problem in the federated system efficiently. Theoretically, we show that the proposed PerFL-RSR reaches a convergence rate of O(1/T), and the proposed estimator is statistically consistent. Thorough experiments and real data analysis are conducted to corroborate the theoretical results of our proposed personalized federated learning method. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.