Communication-Efficient Adaptive Federated Learning

TL;DR

This paper introduces FedCAMS, a novel federated learning method that reduces communication costs and enhances adaptivity, achieving theoretical convergence guarantees and strong empirical performance.

Contribution

FedCAMS is a new communication-efficient adaptive federated learning algorithm with proven convergence in nonconvex settings, addressing key challenges in practical federated learning.

Findings

Achieves the same convergence rate as non-compressed methods

Reduces communication overhead significantly

Performs well across various benchmarks

Abstract

Federated learning is a machine learning training paradigm that enables clients to jointly train models without sharing their own localized data. However, the implementation of federated learning in practice still faces numerous challenges, such as the large communication overhead due to the repetitive server-client synchronization and the lack of adaptivity by SGD-based model updates. Despite that various methods have been proposed for reducing the communication cost by gradient compression or quantization, and the federated versions of adaptive optimizers such as FedAdam are proposed to add more adaptivity, the current federated learning framework still cannot solve the aforementioned challenges all at once. In this paper, we propose a novel communication-efficient adaptive federated learning method (FedCAMS) with theoretical convergence guarantees. We show that in the nonconvex stochastic optimization setting, our proposed FedCAMS achieves the same convergence rate of $O(\frac{1}{\sqrt{TKm}})$ as its non-compressed counterparts. Extensive experiments on various benchmarks verify our theoretical analysis.