Asynchronous Adaptation and Learning over Networks --- Part I: Modeling and Stability Analysis
In this work and the supporting Parts II [2] and III [3], we provide a rather detailed analysis of the stability and performance of asynchronous strategies for solving distributed optimization and adaptation problems over networks. We examine asynchronous networks that are subject to fairly general sources of uncertainties, such as changing topologies, random link failures, random data arrival times, and agents turning on and off randomly. Under this model, agents in the network may stop updating their solutions or may stop sending or receiving information in a random manner and without coordination with other agents. We establish in Part I conditions on the first and second-order moments of the relevant parameter distributions to ensure mean-square stable behavior. We derive in Part II expressions that reveal how the various parameters of the asynchronous behavior influence network performance. We compare in Part III the performance of asynchronous networks to the performance of both centralized solutions and synchronous networks. One notable conclusion is that the mean-square-error performance of asynchronous networks shows a degradation only of the order of $O(\nu)$, where $\nu$ is a small step-size parameter, while the convergence rate remains largely unaltered. The results provide a solid justification for the remarkable resilience of cooperative networks in the face of random failures at multiple levels: agents, links, data arrivals, and topology.
Code (0)
등록된 구현이 없습니다.
Tasks
Distributed OptimizationSimilar Papers 제목 키워드 기반
Asynchronous Adaptation and Learning over Networks - Part II: Performance Analysis
In Part I \cite{Zhao13TSPasync1}, we introduced a fairly general model for asynchronous events over adaptive networks including random topologies, random link failures, random data arrival times, and agents turning on an…
Distributed OptimizationAsynchronous Adaptation and Learning over Networks - Part III: Comparison Analysis
In Part II [3] we carried out a detailed mean-square-error analysis of the performance of asynchronous adaptation and learning over networks under a fairly general model for asynchronous events including random topologie…
Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning
Asynchronous stochastic approximations (SAs) are an important class of model-free algorithms, tools and techniques that are popular in multi-agent and distributed control scenarios. To counter Bellman's curse of dimensio…
Multi-agent Reinforcement LearningPolicy Gradient MethodsMixTTE: Multi-Level Mixture-of-Experts for Scalable and Adaptive Travel Time Estimation
Accurate Travel Time Estimation (TTE) is critical for ride-hailing platforms, where errors directly impact user experience and operational efficiency. While existing production systems excel at holistic route-level depen…
Travel Time EstimationIncremental LearningA General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates
We propose a novel framework to study asynchronous federated learning optimization with delays in gradient updates. Our theoretical framework extends the standard FedAvg aggregation scheme by introducing stochastic aggre…
Federated Learning