Communication-Efficient Learning of Deep Networks from Decentralized\n Data

Modern mobile devices have access to a wealth of data suitable for learning\nmodels, which in turn can greatly improve the user experience on the device.\nFor example, language models can improve speech recognition and text entry, and\nimage models can automatically select good photos. However, this rich data is\noften privacy sensitive, large in quantity, or both, which may preclude logging\nto the data center and training there using conventional approaches. We\nadvocate an alternative that leaves the training data distributed on the mobile\ndevices, and learns a shared model by aggregating locally-computed updates. We\nterm this decentralized approach Federated Learning.\n We present a practical method for…