• Salman Avestimehr


Our work on FedML is nominated for best paper award at NeurIPS wrokshop on Scalability, Privacy, and Security in Federated Learning (SpicyFL'20). FedML is an open research library and benchmark that facilitates the development of new federated learning algorithms and fair performance comparisons. FedML supports three computing paradigms (distributed training, mobile on-device training, and standalone simulation) for users to conduct experiments in different system environments. FedML also promotes diverse algorithmic research with flexible and generic API design and reference baseline implementations.

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