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.
翻译:关于数据孤立岛屿和隐私问题,联邦学习广泛引起人们的极大兴趣,因为联邦学习使客户能够在不与第三方分享任何数据的情况下,利用当地数据合作培训全球模型,但是,现有的联邦学习框架总是需要复杂的条件配置(例如,像荷兰国家空间研究所这样的独立图形卡的复杂驱动器配置,汇编环境),给大规模开发和部署带来很多不便。为了便利部署联邦学习和相关应用程序的实施,我们创新地提议了WebFed,这是一个以浏览器为基础的新的联邦学习框架,利用浏览器的功能(例如,跨平台、爪哇空间绘图功能),并通过地方差异隐私机制加强隐私保护。最后,我们进行了关于多种装置的实验,以评价拟议的网络框架的性能。