DRIVE: One-bit Distributed Mean EstimationDownload PDF

Published: 09 Nov 2021, Last Modified: 22 Oct 2023NeurIPS 2021 PosterReaders: Everyone
Keywords: Distributed Mean Estimation, Stochastic Approximation, Federated Learning, Distributed Leaning, Communication Efficiency, Bandwidth Reduction
TL;DR: Novel mathematical results and new algorithms for distributed mean estimation using one bit per coordinate.
Abstract: We consider the problem where $n$ clients transmit $d$-dimensional real-valued vectors using $d(1+o(1))$ bits each, in a manner that allows the receiver to approximately reconstruct their mean. Such compression problems naturally arise in distributed and federated learning. We provide novel mathematical results and derive computationally efficient algorithms that are more accurate than previous compression techniques. We evaluate our methods on a collection of distributed and federated learning tasks, using a variety of datasets, and show a consistent improvement over the state of the art.
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Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 5 code implementations](https://www.catalyzex.com/paper/arxiv:2105.08339/code)
Code: https://github.com/amitport/DRIVE-One-bit-Distributed-Mean-Estimation
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