# Federated Primal Dual Fixed Point Algorithm

arXiv: https://arxiv.org/abs/2305.13604 · HTML: https://mlsanity.com/paper/2305.13604/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2023.

## Built on by (most cited first, 1 of 1)

- [A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data](https://mlsanity.com/paper/2410.20659/) (2024)

## Abstract (arXiv)

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) citePDFP 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(frac1k) 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.