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

This research proposes a new method for multi-agent reinforcement learning (a type of AI where multiple independent agents learn to make decisions together) in electricity trading systems that protects user privacy while maintaining system efficiency. The approach uses dynamic differential privacy (a mathematical technique that adds controlled noise to data to hide individual information), personalized privacy assessments, and a denoising network (a neural network that removes the noise added for privacy) to balance each user's different privacy needs with the overall performance of the trading system.