Personalized Federated Learning Based on Sparsity Regularized Bi-level Optimization
LIU Xi1, LIU Bo2, JI Fanfan3, YUAN Xiaotong4,5
1. School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044; 2. Walmart Global Tech Hub, Sunnyvale, CA 94086, USA; 3. School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044; 4. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023; 5. School of Intelligence Science and Technology, Nanjing University, Suzhou 215163
Abstract：Personalized federated learning focuses on providing personalized model for each client, aiming to improve the processing performance on statistically heterogeneous data. However, most existing personalized federated learning algorithms enhance the performance of personalized models at the cost of increasing the number of client parameters and making computation more complex. To address this issue, a personalized federated learning algorithm based on sparsity regularized bi-level optimization(pFedSRB) is proposed in this paper. The l1 norm sparse regularization is introduced into the personalized update of each client to enhance the sparsity of the personalized model, avoid unnecessary parameter updates of clients, and reduce model complexity. The personalized federated learning problem is formulated as a bi-level optimization problem, and the inner-level optimization of pFedSRB is solved by the alternating direction method of multipliers to improve the learning speed. Experiments on four federated learning benchmark datasets demonstrate that pFedSRB performs well on heterogeneous data , effectively improving model performance while reducing the time and memory costs required for training.
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