Scatterbrained: A flexible and expandable pattern for decentralized machine learning
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 14 references that are in the map. First posted on arXiv 14 Dec 2021.
Builds on
9 of 14 references.
References · 9
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
- 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 115
- 2019Peer-to-peer Federated Learning on Graphscited by 85
- 2020Local and Central Differential Privacy for Robustness and Privacy in Federated Learningcited by 72
- 2019Decentralized Federated Learning: A Segmented Gossip Approachcited by 63
- 2017Collaborative Deep Learning in Fixed Topology Networkscited by 60
- 2018GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descentcited by 19
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Citing papers per year: 2022: 1; 2023: 1.
2 papers.