WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy

• URL: http://arxiv.org/abs/2110.11646v1
• Date: Fri, 22 Oct 2021 08:18:41 GMT
• Title: WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy
• Authors: Zhuotao Lian, Qinglin Yang, Qingkui Zeng, Chunhua Su
• Abstract summary: WebFed is a novel browser-based federated learning framework that takes advantage of the browser's features. We conduct experiments on heterogeneous devices to evaluate the performance of the proposed WebFed framework.
• Score: 0.7837881800517109
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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