Multi-Agent Energy Trading With Privacy Heterogeneity: A Denoise Dynamic Differential Privacy Multi-Agent Reinforcement Learning Method

Summary

Researchers propose a dynamic differential privacy scheme for multi-agent electricity trading in smart grids, where agents have heterogeneous privacy needs. The method uses a weighted mutual information risk metric, a Markov decision process model, and adaptive noise injection with a denoising network, and the authors prove it satisfies ε-differential privacy. In simulations it improves privacy protection by 79.11% and 77.76% over fixed-noise and personalized-noise benchmarks, and the denoising network removes 50.97% of the cost increase caused by noise.