Gradient Descent with Compressed Iterates

Ahmed Khaled; Peter Richtárik

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arXiv · 1909.04716

Gradient Descent with Compressed Iterates

Ahmed Khaled·Peter Richtárik

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Abstract

We propose and analyze a new type of stochastic first order method: gradient descent with compressed iterates (GDCI). GDCI in each iteration first compresses the current iterate using a lossy randomized compression technique, and subsequently takes a gradient step. This method is a distillation of a key ingredient in the current practice of federated learning, where a model needs to be compressed by a mobile device before it is sent back to a server for aggregation. Our analysis provides a step towards closing the gap between the theory and practice of federated learning, and opens the possibility for many extensions.

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Ahmed Khaled, Peter Richtárik. 2019-09-10. Gradient Descent with Compressed Iterates. https://arxiv.org/abs/1909.04716

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