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Federated machine learning is a technique for training a model acrossmultiple devices without exchanging data between them. Because data remainslocal to each compute node, federated learning is well-suited for use-cases infields where data is carefully controlled, such as medicine, or in domains withbandwidth constraints. One weakness of this approach is that most federatedlearning tools rely upon a central server to perform workload delegation and toproduce a single shared model. Here, we suggest a flexible framework fordecentralizing the federated learning pattern, and provide an open-source,reference implementation compatible with PyTorch.

Scatterbrained: A flexible and expandable pattern for decentralized machine learning