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Project FLUTE

Breaking barriers for Federated Learning research at scale

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This diagram shows a payload exchange between a server, inside Worker 0, and clients that live inside Workers 2 and 3. First, the server pushes the central ML model plus the clients’ data to Workers 2 and 3. Then, each client trains the model with their local data. Finally, the clients send the pseudo-gradients of this new model back to the server for aggregation and the creation of a new global model.
Microsoft Research Blog

| Dimitrios Dimitriadis, Mirian Hipolito Garcia, Daniel Eduardo Madrigal Diaz, Andre Manoel, and Robert Sim

Federated learning has become a major area of machine learning (ML) research in recent years due to its versatility in training complex models over massive amounts of data without the need to share that data with a centralized entity. However,…