论文标题
FLHUB：联合学习模型共享服务
FLHub: a Federated Learning model sharing service
论文作者
论文摘要
由于易于使用的深度学习库（例如Tensorflow和Pytorch）很受欢迎，因此开发机器学习模型已变得方便。由于集中式机器学习的隐私问题，最近在分布式计算框架中联合学习引起了人们的关注。中央服务器不会从联合学习中收集敏感和个人数据，但仅汇总模型参数。尽管联合学习有助于保护隐私，但机器学习开发人员很难共享可以用于不同域应用程序的模型。在本文中，我们提出了一个名为Federated Learning Hub（FLHUB）的联合学习模型共享服务。用户可以上传，下载和贡献与GitHub类似的其他开发人员开发的模型。我们证明，分叉模型可以比现有模型更快地完成培训，并且每个联合回合的学习进展都更快。
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.
