Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challenges brought by various types of heterogeneity, including the heterogeneity in participants' local data, resources, behaviors and learning goals. To fill this gap, in this paper, we propose a novel FL platform, named FederatedScope, which employs an event-driven architecture to provide users with great flexibility to independently describe the behaviors of different participants. Such a design makes it easy for users to describe participants with various local training processes, learning goals and backends, and coordinate them into an FL course with synchronous or asynchronous training strategies. Towards an easy-to-use and flexible platform, FederatedScope enables rich types of plug-in operations and components for efficient further development, and we have implemented several important components to better help users with privacy protection, attack simulation and auto-tuning. We have released FederatedScope at https://github.com/alibaba/FederatedScope to promote academic research and industrial deployment of federated learning in a wide range of scenarios.
翻译:尽管现有的联邦学习平台(FL)在为发展提供基础设施方面取得了显著进展,但这些平台可能无法很好地应对不同类型差异带来的挑战,包括参与者当地数据、资源、行为和学习目标的差异。为了填补这一差距,我们在本文件中提议了一个名为FreedScope的新的FL平台,名为FreedScope,它使用一个事件驱动的结构,为用户独立描述不同参与者的行为提供了极大的灵活性。这种设计使用户能够方便地描述参与者的各种当地培训进程、学习目标和后端,并将它们与同步或无同步的培训战略协调到FL课程中。为了建立一个容易使用的灵活平台,FreedScope为高效的进一步发展提供了丰富种类的插座操作和组件,我们实施了几个重要组成部分,以更好地帮助用户保护隐私、攻击模拟和自动调整。我们已经在https://github.com/alibaba/FreedScope发布了FreedScope,以促进学术研究和在广泛范围内的反馈情景中的工业应用。