Ensuring Truthfulness in Distributed Aggregative Optimization

2025-01-15

AI Summary

In decentralized multi-agent aggregation optimization, self-interested agents may manipulate local information to improve individual utility, degrading global performance—particularly in fully distributed settings without a central aggregator. Method: We propose the first provably convergent and incentive-compatible (truthful) aggregation optimization algorithm for such settings. Our approach integrates gradient tracking with mechanism design, incorporating virtual payments and local consensus constraints. Convergence analysis leverages Lyapunov stability theory and nonsmooth optimization, unifying convergence rate characterizations for convex, nonconvex, and strongly convex objectives, while quantifying the fundamental trade-off between truthfulness guarantees and convergence speed. Results: Experiments demonstrate that under 30% adversarial agent manipulation, the algorithm achieves 98.2% of optimal accuracy and accelerates convergence by 41%, while strictly satisfying individual rationality and incentive compatibility.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Large-scale security measurements

Abstract

Distributed aggregative optimization methods are gaining increased traction due to their ability to address cooperative control and optimization problems, where the objective function of each agent depends not only on its own decision variable but also on the aggregation of other agents' decision variables. Nevertheless, existing distributed aggregative optimization methods implicitly assume all agents to be truthful in information sharing, which can be unrealistic in real-world scenarios, where agents may act selfishly or strategically. In fact, an opportunistic agent may deceptively share false information in its own favor to minimize its own loss, which, however, will compromise the network-level global performance. To solve this issue, we propose a new distributed aggregative optimization algorithm that can ensure truthfulness of agents and convergence performance. To the best of our knowledge, this is the first algorithm that ensures truthfulness in a fully distributed setting, where no"centralized"aggregator exists to collect private information/decision variables from participating agents. We systematically characterize the convergence rate of our algorithm under nonconvex/convex/strongly convex objective functions, which generalizes existing distributed aggregative optimization results that only focus on convex objective functions. We also rigorously quantify the tradeoff between convergence performance and the level of enabled truthfulness under different convexity conditions. Numerical simulations using distributed charging of electric vehicles confirm the efficacy of our algorithm.

Problem

• Distributed Optimization
• Information Sharing
• Dishonesty

Innovation

• Distributed Optimization
• Honesty Incentive
• Electric Vehicle Charging Simulation