Federated Learning via Inexact ADMM

Abstract--One of the crucial issues in federated learning is how to develop efficient optimization algorithms. Most of the current ones require full device participation and/or impose strong assumptions for convergence. Different from the widely-used gradient descentbased algorithms, in this paper, we develop an inexact alternating direction method of multipliers (ADMM), which is both computationand communication-efficient, capable of combating the stragglers' effect, and convergent under mild conditions. Furthermore, it has a high numerical performance compared with several state-of-the-art algorithms for federated learning. This idea has been extensively exploited in the [4], [5], [6], digital health [7], and mobile edge and over-theair stochastic gradient descent (SGD) algorithms, such as the computing [8], [9], [10], [11].