Information-Theoretic Perspective of Federated Learning

15 Nov 2019Linara AdilovaJulia RosenzweigMichael Kamp

An approach to distributed machine learning is to train models on local datasets and aggregate these models into a single, stronger model. A popular instance of this form of parallelization is federated learning, where the nodes periodically send their local models to a coordinator that aggregates them and redistributes the aggregation back to continue training with it... (read more)

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