Communication-Efficient Learning of Deep Networks from Decentralized Data
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- Communication-Efficient Learning of Deep Networks from Decentralized Data arXiv · published 2016-02-17 · arXiv abstract page, retrieved for the candidate pack on 2026-10-28 (SHA-256 2791d18dab8a77a8160f4e5d41f8caeb53b5b800e1c46308637d24cbcad16cd9). Submission history as arXiv records it (UTC): v1 17 February 2016; v2 21 October 2016; v3 28 February 2017; v4 26 January 2023. The page serves the latest version; each version is a separate expression, and only the one a record names was read.
- Communication-Efficient Learning of Deep Networks from Decentralized Data arXiv · published 2016-02-17 · arXiv:1602.05629v1, submitted 17 February 2016 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 5e5f5264bc409fbe1c43a6ac011b701e14b04fdf784a0deae47b39122819402c). The author list is the byline as printed on the first page.
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- In its own wordsThe authors advocate leaving training data distributed on mobile devices and learning a shared model by aggregating locally computed updates, an approach the paper calls Federated Learning.“We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates.” arXiv v1 (titled "Federated Learning of Deep Networks using Model Averaging"), PDF p. 1, abstract2016-02-17 · publication · Stated in source · Supported Inspect claim & evidence ↗
- In its own wordsThe paper presents a method for federated learning of deep networks that the authors state is robust to unbalanced and non-IID data distributions.“We present a practical method for the federated learning of deep networks that proves robust to the unbalanced and non-IID data distributions that naturally arise.” arXiv v1 (titled "Federated Learning of Deep Networks using Model Averaging"), PDF p. 1, abstract2016-02-17 · publication · Stated in source · Supported Inspect claim & evidence ↗