FLHub: a Federated Learning model sharing service
This addresses the problem of model sharing for machine learning developers in federated learning, but it is incremental as it adapts existing concepts like GitHub to this domain.
The paper tackles the difficulty of sharing models in federated learning by proposing FLHub, a service for uploading, downloading, and contributing models, demonstrating that forked models train faster and progress more quickly per federated round.
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.