Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies

TL;DR

This survey reviews recent system optimization strategies for federated learning, focusing on improving training efficiency amidst challenges like client heterogeneity and system configuration complexity.

Contribution

It systematically categorizes and analyzes recent methods addressing FL training challenges across client selection, configuration, and reporting phases.

Findings

Identifies key challenges in achieving fast FL training.

Summarizes recent optimization techniques and system tools.

Highlights gaps and future directions in FL system research.

Abstract

The increasing demand for privacy-preserving collaborative learning has given rise to a new computing paradigm called federated learning (FL), in which clients collaboratively train a machine learning (ML) model without revealing their private training data. Given an acceptable level of privacy guarantee, the goal of FL is to minimize the time-to-accuracy of model training. Compared with distributed ML in data centers, there are four distinct challenges to achieving short time-to-accuracy in FL training, namely the lack of information for optimization, the tradeoff between statistical and system utility, client heterogeneity, and large configuration space. In this paper, we survey recent works in addressing these challenges and present them following a typical training workflow through three phases: client selection, configuration, and reporting. We also review system works including measurement studies and benchmarking tools that aim to support FL developers.