Work

A Dual Accelerated Method for Online Stochastic Distributed Averaging: From Consensus to Decentralized Policy Evaluation

Year: 2025

Type: article

Abstract: Motivated by decentralized sensing and policy evaluation problems, we consider a particular type of distributed stochastic optimization problem over a network, called the online stochastic distributed averaging problem. We design and analyze a dual-based, Polyak–Ruppert averaged method for this problem. We show that the proposed algorithm attains an accelerated deterministic error depending optimally on the condition number of the network, and also that it has optimal stochastic error. This improves on the guarantees of state-of-the-art distributed stochastic optimization algorithms when specialized to this setting, and yields—among other things—corollaries for decentralized policy evaluation. Numerical experiments validate our theoretical results and demonstrate that our approach outperforms existing methods in finite-sample scenarios on several natural network topologies.

Source: IEEE Transactions on Automatic Control

DOI: 10.1109/tac.2025.3562997

Authors: Sheng Zhang, Ashwin Pananjady, Justin Romberg

Institution: Georgia Institute of Technology

Language: English

Topic: Distributed Control Multi-Agent Systems

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Awards: G5202178799, CIF: Medium: Learning, refining, and understanding models through relational feedback, Nonparametric Methodology for Learning from People: Inference, Algorithms, and Optimality