This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...
We consider a collaborative decision-making framework where heterogeneous agents receive streaming and partially informative observations. We consider two asynchronous scenarios that differ based on the agents' participation patterns and the fusion center' ...
This work examines a social learning problem, where dispersed agents connected through a network topology interact locally to form their opinions (beliefs) as regards certain hypotheses of interest. These opinions evolve over time, since the agents collect ...
This work introduces and studies the convergence of a stochastic diffusion-optimistic learning (DOL) strategy for solving distributed nonconvex (NC) and Polyak-Lojasiewicz (PL) min-max optimization problems. Problems of this type are of interest due to a w ...
Institute of Electrical and Electronics Engineers Inc.2024
The DEF-ATC (Differential Error Feedback - Adapt Then Combine) approach is a novel strategy to address decentralized learning and optimization problems under communication constraints. The strategy blends differential quantization and error feedback to mit ...
Institute of Electrical and Electronics Engineers Inc.2024
Communication-constrained algorithms for decentralized learning and optimization rely on the exchange of quantized signals coupled with local updates. In this context, differential quantization is an effective technique to mitigate the negative impact of q ...
In this work we derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem. Agents employ the recently proposed ACTC (adapt-compress-then-combine) diffu ...
This article considers a network of agents interested in solving a classification t ask. The datasets available to accomplish the task are heterogeneous and dispersed across the agents. Each agent is inter-ested in discriminating among the 'inner' hypothes ...
Traditional social learning frameworks consider environments with a homogeneous state where each agent receives observations conditioned on the same hypothesis. In this work, we study the distributed hypothesis testing problem for graphs with a community s ...
Institute of Electrical and Electronics Engineers Inc.2024
Non-Bayesian social learning is a framework for distributed hypothesis testing aimed at learning the true state of the environment. Traditionally, the agents are assumed to receive observations conditioned on the same true state, although it is also possib ...
IEEE Institute of Electrical and Electronics Engineers2024
