Composite federated learning with heterogeneous data

• URL: http://arxiv.org/abs/2309.01795v1
• Date: Mon, 4 Sep 2023 20:22:57 GMT
• Title: Composite federated learning with heterogeneous data
• Authors: Jiaojiao Zhang, Jiang Hu, Mikael Johansson
• Abstract summary: We propose a novel algorithm for solving the composite Federated Learning (FL) problem. This algorithm manages non-smooth regularization by strategically decoupling the proximal operator and communication, and addresses client drift without any assumptions about data similarity. We prove that our algorithm converges linearly to a neighborhood of the optimal solution and demonstrate the superiority of our algorithm over state-of-the-art methods in numerical experiments.
• Score: 11.40641907024708
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel algorithm for solving the composite Federated Learning (FL) problem. This algorithm manages non-smooth regularization by strategically decoupling the proximal operator and communication, and addresses client drift without any assumptions about data similarity. Moreover, each worker uses local updates to reduce the communication frequency with the server and transmits only a $d$-dimensional vector per communication round. We prove that our algorithm converges linearly to a neighborhood of the optimal solution and demonstrate the superiority of our algorithm over state-of-the-art methods in numerical experiments.

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