Federated Learning: Strategies for Improving Communication Efficiency

ICLR 2018 Jakub KonečnýH. Brendan McMahanFelix X. YuPeter RichtárikAnanda Theertha SureshDave Bacon

Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slow network connections. We consider learning algorithms for this setting where on each round, each client independently computes an update to the current model based on its local data, and communicates this update to a central server, where the client-side updates are aggregated to compute a new global model... (read more)

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