Federated Learning: Strategies for Improving Communication Efficiency

Jakub Konečný, H. Brendan McMahan, Felix X. Yu et al.

2016 · arXiv (Cornell University) · 3,050 citations

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. The typical clients in this setting are mobile phones, and communicati…

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