FLHub: a Federated Learning model sharing service

TL;DR

FLHub is a platform enabling developers to share, fork, and collaboratively improve federated learning models, accelerating training and learning processes while preserving privacy.

Contribution

The paper introduces FLHub, a novel federated learning model sharing service similar to GitHub, facilitating model sharing and collaborative development.

Findings

Forked models train faster than original models.

Learning progresses more quickly per federated round.

Model sharing accelerates federated learning development.

Abstract

As easy-to-use deep learning libraries such as Tensorflow and Pytorch are popular, it has become convenient to develop machine learning models. Due to privacy issues with centralized machine learning, recently, federated learning in the distributed computing framework is attracting attention. The central server does not collect sensitive and personal data from clients in federated learning, but it only aggregates the model parameters. Though federated learning helps protect privacy, it is difficult for machine learning developers to share the models that they could utilize for different-domain applications. In this paper, we propose a federated learning model sharing service named Federated Learning Hub (FLHub). Users can upload, download, and contribute the model developed by other developers similarly to GitHub. We demonstrate that a forked model can finish training faster than the existing model and that learning progressed more quickly for each federated round.