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arXiv · 2112.07718

Scatterbrained: A flexible and expandable pattern for decentralized machine learning

Miller Wilt·Jordan K. Matelsky·Andrew S. Gearhart
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Abstract

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.

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Miller Wilt, Jordan K. Matelsky, Andrew S. Gearhart. 2021-12-14. Scatterbrained: A flexible and expandable pattern for decentralized machine learning. https://arxiv.org/abs/2112.07718

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