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.
@article{arxiv.1909.04716,
title = {Gradient Descent with Compressed Iterates},
author = {Ahmed Khaled and Peter Richtárik},
journal= {arXiv preprint arXiv:1909.04716},
year = {2020}
}
Comments
NeurIPS 2019 Workshop on Federated Learning for Data Privacy and Confidentiality. 10 pages, 1 algorithm, 1 theorem, 5 lemmas