Federated Learning (FL) is a method of training machine learning models on
private data distributed over a large number of possibly heterogeneous clients
such as mobile phones and IoT devices. In this work, we propose a new federated
learning framework named HeteroFL to address heterogeneous clients equipped
with very different computation and communication capabilities. Our solution
can enable the training of heterogeneous local models with varying computation
complexities and still produce a single global inference model. For the first
time, our method challenges the underlying assumption of existing work that
local models have to share the same architecture as the global model. We
demonstrate several strategies to enhance FL training and conduct extensive
empirical evaluations, including five computation complexity levels of three
model architecture on three datasets. We show that adaptively distributing
subnetworks according to clients' capabilities is both computation and
communication efficient.

本文提出名为 HeteroFL 的新型联邦学习框架，旨在解决具有非常不同计算和通信能力的异构客户端的问题，并通过几种策略增强 FL 训练。通过在三个数据集上进行五个计算复杂性级别的三种模型架构的广泛实证评估，通过自适应分配子网络来提高 FL 训练的计算和通信效率。