Journal ArticleDOI
Distributed Energy-Aware Diffusion Least Mean Squares: Game-Theoretic Learning
TLDR
The convergence analysis shows that the parameter estimates weakly converge to the true parameter across the network, yet the global activation behavior along the way tracks the set of correlated equilibria of the underlying activation control game.Abstract:
This paper presents a game-theoretic approach to node activation control in parameter estimation via diffusion least mean squares (LMS). Nodes cooperate by exchanging estimates over links characterized by the connectivity graph of the network. The energy-aware activation control is formulated as a noncooperative repeated game where nodes autonomously decide when to activate based on a utility function that captures the trade-off between individual node's contribution and energy expenditure. The diffusion LMS stochastic approximation is combined with a game-theoretic learning algorithm such that the overall energy-aware diffusion LMS has two timescales: the fast timescale corresponds to the game-theoretic activation mechanism, whereby nodes distributively learn their optimal activation strategies, whereas the slow timescale corresponds to the diffusion LMS. The convergence analysis shows that the parameter estimates weakly converge to the true parameter across the network, yet the global activation behavior along the way tracks the set of correlated equilibria of the underlying activation control game.read more
Citations
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Adaptation, Learning, and Optimization Over Networks
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Adaptive Networks
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Diffusion LMS Over Multitask Networks
TL;DR: This paper conducts a theoretical analysis on the stochastic behavior of diffusion LMS in the case where the single-task hypothesis is violated and proposes an unsupervised clustering strategy that allows each node to select, via adaptive adjustments of combination weights, the neighboring nodes with which it can collaborate to estimate a common parameter vector.
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Artificial Intelligence for Vehicle-to-Everything: A Survey
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On the Learning Behavior of Adaptive Networks—Part I: Transient Analysis
Jianshu Chen,Ali H. Sayed +1 more
TL;DR: A detailed transient analysis of the learning behavior of multiagent networks reveals how combination policies influence the learning process of networked agents, and how these policies can steer the convergence point toward any of many possible Pareto optimal solutions.
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