Federated Learning Hyper-Parameter Tuning from a System Perspective

TL;DR

FedTune is an automatic hyper-parameter tuning algorithm for federated learning that adapts to diverse system requirements, reducing training overhead and easing the burden on practitioners.

Contribution

This paper introduces FedTune, a novel system-aware hyper-parameter tuning method that dynamically adjusts FL hyper-parameters during training for improved efficiency.

Findings

Achieves 8.48%-26.75% reduction in system overhead.

Easily integrates into existing FL systems.

Effective across diverse applications and algorithms.

Abstract

Federated learning (FL) is a distributed model training paradigm that preserves clients' data privacy. It has gained tremendous attention from both academia and industry. FL hyper-parameters (e.g., the number of selected clients and the number of training passes) significantly affect the training overhead in terms of computation time, transmission time, computation load, and transmission load. However, the current practice of manually selecting FL hyper-parameters imposes a heavy burden on FL practitioners because applications have different training preferences. In this paper, we propose FedTune, an automatic FL hyper-parameter tuning algorithm tailored to applications' diverse system requirements in FL training. FedTune iteratively adjusts FL hyper-parameters during FL training and can be easily integrated into existing FL systems. Through extensive evaluations of FedTune for diverse applications and FL aggregation algorithms, we show that FedTune is lightweight and effective, achieving 8.48%-26.75% system overhead reduction compared to using fixed FL hyper-parameters. This paper assists FL practitioners in designing high-performance FL training solutions. The source code of FedTune is available at https://github.com/DataSysTech/FedTune.