Federated machine learning is a technique for training a model across multiple devices without exchanging data between them. Because data remains local to each compute node, federated learning is well-suited for use-cases in fields where data is carefully controlled, such as medicine, or in domains with bandwidth constraints. One weakness of this approach is that most federated learning tools rely upon a central server to perform workload delegation and to produce a single shared model. Here, we suggest a flexible framework for decentralizing the federated learning pattern, and provide an open-source, reference implementation compatible with PyTorch.
@article{arxiv.2112.07718,
title = {Scatterbrained: A flexible and expandable pattern for decentralized machine learning},
author = {Miller Wilt and Jordan K. Matelsky and Andrew S. Gearhart},
journal= {arXiv preprint arXiv:2112.07718},
year = {2021}
}
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Code and documentation is available at https://github.com/JHUAPL/scatterbrained