Decentralized Communication-Efficient Multi-Task Representation Learning
Information Theory
2025-08-27 v7 math.IT
Abstract
This work develops a provably accurate fully-decentralized alternating projected gradient descent (GD) algorithm for recovering a low rank (LR) matrix from mutually independent projections of each of its columns, in a fast and communication-efficient fashion. To our best knowledge, this work is the first attempt to develop a provably correct decentralized algorithm (i) for any problem involving the use of an alternating projected GD algorithm; (ii) and for any problem in which the constraint set to be projected to is a non-convex set.
Cite
@article{arxiv.2204.08117,
title = {Decentralized Communication-Efficient Multi-Task Representation Learning},
author = {Shana Moothedath and Namrata Vaswani},
journal= {arXiv preprint arXiv:2204.08117},
year = {2025}
}