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DASA: Delay-Adaptive Multi-Agent Stochastic Approximation
March 27, 2024, 4:45 a.m. | Nicolo Dal Fabbro, Arman Adibi, H. Vincent Poor, Sanjeev R. Kulkarni, Aritra Mitra, George J. Pappas
stat.ML updates on arXiv.org arxiv.org
Abstract: We consider a setting in which $N$ agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server. We assume that the up-link transmissions to the server are subject to asynchronous and potentially unbounded time-varying delays. To mitigate the effect of delays and stragglers while reaping the benefits of distributed computation, we propose \texttt{DASA}, a Delay-Adaptive algorithm for multi-agent Stochastic Approximation. We provide a finite-time analysis …
abstract acting agent agents aim approximation arxiv asynchronous cs.ai cs.ro cs.sy delay eess.sy math.oc multi-agent server stat.ml stochastic type
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