  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 descent-based algorithms, in this paper, we develop an
inexact alternating direction method of multipliers (ADMM), which is both
computation- and 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.

Optimization and Control;Machine Learning