Year: 2024
Type: conference-paper
Abstract: We consider a collection of intelligent agents with the objective of learning distinct, but related tasks. Classical algorithms for federated and decentralized learning employ consensus mechanisms to learn a single, common model across the different agents and tasks. In recent years, there has been increasing interest in multitask or meta-learning techniques to develop personalized learning algorithms over networks, though most studies focus on the federated setting with single fusion center. We develop a fully decentralized strategy for personalized learning over networks based on proximal regularization and explore relations with alternatives based on quadratic regularization or meta-learning. Numerical results demonstrate the benefit of the proposed approach.
Authors: Stefan Vlaski, Roula Nassif
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4409156563