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.

本研究提出了一个名为 Federated Learning Hub（FLHub）的联邦学习模型共享服务，用户可以上传、下载、贡献由其他开发者开发的模型，类似于 GitHub。我们证明，分叉的模型比现有模型更快地完成了训练，并且每个联邦循环的学习速度更快。