Communication-Efficient Learning of Deep Networks from Decentralized Data

Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. 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.