Communication-efficient learning of deep networks from decentralized data.¶
McMahan, B., Moore, Ramage, Hampson, & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 1273-1282.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Aggregation
- In contemporary practice, aggregation appears in quarterly financial rollups, ensemble model training, federated learning, and portfolio-return reporting; the federated-averaging case in particular was formalized by McMahan et al. (2017) for training deep networks across decentralized data without centralizing the underlying records.
This sourceIntroduces federated averaging: aggregating locally-trained model parameters without centralizing raw data.
Supported in partVerified against the work's full text
The paper introduces federated learning and federated averaging, training deep networks from updates computed on decentralized devices, which backs the federated-averaging clause of the claim.
“We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures …”
- In contemporary practice, aggregation appears in quarterly financial rollups, ensemble model training, federated learning, and portfolio-return reporting; the federated-averaging case in particular was formalized by McMahan et al. (2017) for training deep networks across decentralized data without centralizing the underlying records.
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