Publications · Preprint · arXiv 2025
DIGing-SGLD: decentralized and scalable Langevin sampling over time-varying networks
arXiv preprint, 2025.
In brief
Agents holding different parts of the data want to sample from the joint posterior without pooling it, over a network whose links change over time — a setting in which earlier decentralized Langevin methods drift away from the target. DIGing-SGLD adds gradient tracking, so each agent follows an estimate of the network-average gradient; this removes the network-induced bias and yields the first finite-time Wasserstein guarantees for time-varying networks, matching centralized rates.
Cite
@misc{bajwa2025diging,
title = {DIGing-SGLD: decentralized and scalable Langevin sampling over time-varying networks},
author = {Waheed U. Bajwa and Mert Gürbüzbalaban and Mustafa Ali Kutbay and Lingjiong Zhu and Muhammad Zulqarnain},
year = {2025},
howpublished = {arXiv preprint arXiv:2511.12836},
}Relatedsame topics