28. FEDERATED LEARNING FOR PRIVACY.pdf

Decentralized energy markets require advanced artificial intelligence (AI) models for tasks such as load forecasting, demand response optimization, and dynamic pricing. However, sharing raw energy consumption and generation data across multiple entities raises significant privacy and security concerns. Federated learning (FL) offers a solution by enabling collaborative model training without centralizing sensitive datasets. This paper presents a framework for implementing FL in decentralized energy markets, integrating blockchain for trust management and hybrid cloud-edge infrastructure for scalability. Two tables compare traditional centralized AI training with FL approaches and summarize performance benchmarks. Results indicate that FL enhances privacy, reduces regulatory risks, and maintains competitive AI performance in decentralized environments.