Communication-Efficient ADMM-based Federated Learning

2021/10/28 by Shenglong Zhou, Geoffrey Ye Li, Zhou, Shenglong +1 · 1 citation
Computer Science · Engineering · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #Wireless Communication Security Techniques

paper · pdf · doi:10.48550/arxiv.2110.15318

openalex publication_date 2021/10/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Federated learning has shown its advances over the last few years but is facing many challenges, such as how algorithms save communication resources, how they reduce computational costs, and whether they converge. To address these issues, this paper proposes exact and inexact ADMM-based federated learning. They are not only communication-efficient but also converge linearly under very mild conditions, such as convexity-free and irrelevance to data distributions. Moreover, the inexact version has low computational complexity, thereby alleviating the computational burdens significantly.

Citations

• LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning

Cited by

• Communication-Efficient Distributed Asynchronous ADMM

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