Abstract
Federated Learning is a distributed machine learning approach which enables
model training on a large corpus of decentralized data. We have built a
scalable production system for Federated Learning in the domain of mobile
devices, based on TensorFlow. In this paper, we describe the resulting
high-level design, sketch some of the challenges and their solutions, and touch
upon the open problems and future directions.
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