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Although remarkable progress has been made by existing federated learning(FL) platforms to provide infrastructures for development, these platforms maynot well tackle the challenges brought by various types of heterogeneity,including the heterogeneity in participants' local data, resources, behaviorsand learning goals. To fill this gap, in this paper, we propose a novel FLplatform, named FederatedScope, which employs an event-driven architecture toprovide users with great flexibility to independently describe the behaviors ofdifferent participants. Such a design makes it easy for users to describeparticipants with various local training processes, learning goals andbackends, and coordinate them into an FL course with synchronous orasynchronous training strategies. Towards an easy-to-use and flexible platform,FederatedScope enables rich types of plug-in operations and components forefficient further development, and we have implemented several importantcomponents to better help users with privacy protection, attack simulation andauto-tuning. We have released FederatedScope athttps://github.com/alibaba/FederatedScope to promote academic research andindustrial deployment of federated learning in a wide range of scenarios.

FederatedScope: A Flexible Federated Learning Platform for Heterogeneity