methodology-7
Federated Learning
3260 papers • 126 benchmarks • 313 datasets
Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model. This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.
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Communication-Efficient Learning of Deep Networks from Decentralized Data
H. B. McMahan, Eider Moore, Daniel Ramage, S. Hampson, B. A. Y. Arcas•Tue Feb 16 2016
This work presents a practical method for the federated learning of deep networks based on iterative model averaging, and conducts an extensive empirical evaluation, considering five different model architectures and four datasets.
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