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.
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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.
- 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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