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The paper introduces a Hegselmann-Krause opinion dynamics approach for coalition formation in federated learning to address data heterogeneity in IoT systems, achieving significant error reduction in water consumption forecasting.
This paper formalizes agent coalition formation and inter-agent communication as a cooperative game, proposing marginal-value activation rules and Shapley-based online routing to reduce token costs and improve efficiency in multi-agent LLM systems, with theoretical guarantees and synthetic simulation results.
This paper studies decentralized coalition formation as a dynamical process driven by unilateral exit-and-join decisions, using the Aumann-Dreze value for local payoff evaluation. It establishes equilibrium characterizations, Lyapunov and potential representations, and analyzes the impact of switching/acceptance costs on stability.