Federated Primal Dual Fixed Point Algorithm

Ya-Nan Zhu; Jingwei Liang; Xiaoqun Zhang

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arXiv · 2305.13604

Federated Primal Dual Fixed Point Algorithm

Ya-Nan Zhu·Jingwei Liang·Xiaoqun Zhang

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Abstract

Federated learning (FL) is a distributed learning paradigm that allows several clients to learn a global model without sharing their private data. In this paper, we generalize a primal dual fixed point (PDFP) \cite{PDFP} method to federated learning setting and propose an algorithm called Federated PDFP (FPDFP) for solving composite optimization problems. In addition, a quantization scheme is applied to reduce the communication overhead during the learning process. An $O(\frac{1}{k})$ convergence rate (where $k$ is the communication round) of the proposed FPDFP is provided. Numerical experiments, including graph-guided logistic regression, 3D Computed Tomography (CT) reconstruction are considered to evaluate the proposed algorithm.

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BibTeX RIS

Ya-Nan Zhu, Jingwei Liang, Xiaoqun Zhang. 2023-05-23. Federated Primal Dual Fixed Point Algorithm. https://arxiv.org/abs/2305.13604

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