Federated Learning-Enhanced Distributed Ledger Framework for Electricity Trading Data Assets Rights Confirmations

This paper presents the Federated Learning-Enhanced Distributed Ledger Framework (FL-DLF), a novel approach to address data rights confirmation in the evolving electricity market characterized by distributed energy resources and virtual power plants. The FL-DLF integrates blockchain technology with federated learning to ensure data integrity, security, and privacy, enabling decentralized data processing and model training without compromising sensitive information. By implementing advanced cryptographic methods, access controls, and continuous monitoring, the framework provides a secure and efficient solution for data rights management. Its modular architecture allows for seamless integration with current systems and adaptability to future technologies. The paper’s contributions include proposing a secure ecosystem for data asset ownership verification, introducing a comprehensive technical solution integrating blockchain and encryption, and exploring the use of smart contracts for automated and efficient data transaction processes. The FL-DLF addresses the challenges of data integrity, privacy, and infrastructure dependability, offering a robust solution for the modern electricity market’s complex data rights confirmation needs.