  For data isolated islands and privacy issues, federated learning has been
extensively invoking much interest since it allows clients to collaborate on
training a global model using their local data without sharing any with a third
party. However, the existing federated learning frameworks always need
sophisticated condition configurations (e.g., sophisticated driver
configuration of standalone graphics card like NVIDIA, compile environment)
that bring much inconvenience for large-scale development and deployment. To
facilitate the deployment of federated learning and the implementation of
related applications, we innovatively propose WebFed, a novel browser-based
federated learning framework that takes advantage of the browser's features
(e.g., Cross-platform, JavaScript Programming Features) and enhances the
privacy protection via local differential privacy mechanism. Finally, We
conduct experiments on heterogeneous devices to evaluate the performance of the
proposed WebFed framework.
