FedHe: Heterogeneous Models and Communication-Efficient Federated Learning

TL;DR

FedHe introduces a federated learning approach that enables training heterogeneous models asynchronously while significantly reducing communication costs, outperforming existing methods in efficiency and accuracy.

Contribution

The paper proposes FedHe, a novel federated learning method that supports heterogeneous models and asynchronous training with reduced communication overheads.

Findings

FedHe achieves better accuracy than state-of-the-art algorithms.

FedHe significantly reduces communication overheads.

FedHe supports asynchronous training processes.

Abstract

Federated learning (FL) is able to manage edge devices to cooperatively train a model while maintaining the training data local and private. One common assumption in FL is that all edge devices share the same machine learning model in training, for example, identical neural network architecture. However, the computation and store capability of different devices may not be the same. Moreover, reducing communication overheads can improve the training efficiency though it is still a challenging problem in FL. In this paper, we propose a novel FL method, called FedHe, inspired by knowledge distillation, which can train heterogeneous models and support asynchronous training processes with significantly reduced communication overheads. Our analysis and experimental results demonstrate that the performance of our proposed method is better than the state-of-the-art algorithms in terms of communication overheads and model accuracy.