  Federated learning (FL) is a rapidly growing research field in machine
learning. However, existing FL libraries cannot adequately support diverse
algorithmic development; inconsistent dataset and model usage make fair
algorithm comparison challenging. In this work, we introduce FedML, an open
research library and benchmark to facilitate FL algorithm development and fair
performance comparison. FedML supports three computing paradigms: on-device
training for edge devices, distributed computing, and single-machine
simulation. FedML also promotes diverse algorithmic research with flexible and
generic API design and comprehensive reference baseline implementations
(optimizer, models, and datasets). We hope FedML could provide an efficient and
reproducible means for developing and evaluating FL algorithms that would
benefit the FL research community. We maintain the source code, documents, and
user community at https://fedml.ai.
