Work

Robust Online Learning Over Networks

Year: 2024

Type: article

Abstract: The recent deployment of multiagent networks has enabled the distributed solution of learning problems, where agents cooperate to train a global model without sharing their local, private data. This work specifically targets some prevalent challenges inherent to distributed learning: 1) online training, i.e., the local data change over time; 2) asynchronous agent computations; 3) unreliable and limited communications; and 4) inexact local computations. To tackle these challenges, we apply the distributed operator theoretical (DOT) version of the alternating direction method of multipliers (ADMM), which we call “DOT-ADMM.” We prove that if the DOT-ADMM operator is metric subregular, then it converges with a linear rate for a large class of (not necessarily strongly) convex learning problems toward a bounded neighborhood of the optimal time-varying solution, and characterize how such neighborhood depends on 1)–4). We first derive an easy-to-verify condition for ensuring the metric subregularity of an operator, followed by tutorial examples on linear and logistic regression problems. We corroborate the theoretical analysis with numerical simulations comparing DOT-ADMM with other state-of-the-art algorithms, showing that only the proposed algorithm exhibits robustness to 1)–4).

Source: IEEE Transactions on Automatic Control

DOI: 10.1109/tac.2024.3441723

Authors: Nicola Bastianello, Diego Deplano, Mauro Franceschelli, Karl H. Johansson

Institutions: Digital Futures, KTH Royal Institute of Technology, University of Cagliari

Language: English

Topic: Distributed Control Multi-Agent Systems

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $1,489

Funders: European Commission, Vetenskapsrådet