Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization

26 Feb 2020 Zhize Li Dmitry Kovalev Xun Qian Peter Richtárik

Due to the high communication cost in distributed and federated learning problems, methods relying on compression of communicated messages are becoming increasingly popular. While in other contexts the best performing gradient-type methods invariably rely on some form of acceleration/momentum to reduce the number of iterations, there are no methods which combine the benefits of both gradient compression and acceleration... (read more)

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