WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy
This work addresses deployment challenges for developers and users in federated learning, though it is incremental as it builds on existing federated learning concepts with browser integration.
The authors tackled the problem of complex deployment in federated learning by proposing WebFed, a browser-based framework that simplifies setup and enhances privacy with local differential privacy, achieving cross-platform functionality as demonstrated in experiments on heterogeneous devices.
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.