SAFER: Sparse Secure Aggregation for Federated Learning

29 Jul 2020 Constance Beguier Eric W. Tramel

Federated learning enables one to train a common machine learning model across separate, privately-held datasets via distributed model training. During federated training, only intermediate model parameters are transmitted to a central server which aggregates these parameters to create a new common model, thus exposing only intermediate parameters rather than the training data itself... (read more)

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