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Found 2 papers, 1 papers with code
Project Florida: Federated Learning Made Easy
Federated learning enables model training across devices and silos while the training data remains within its security boundary, by distributing a model snapshot to a client running inside the boundary, running client code to update the model, and then aggregating updated snapshots across many clients in a central orchestrator.
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
We compare the platform with other state-of-the-art platforms and describe available features of FLUTE for experimentation in core areas of active research, such as optimization, privacy, and scalability.
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