SFedChain: blockchain-based federated learning scheme for secure data sharing in distributed energy storage networks.

Area of Science:

• Energy Systems and Smart Grids
• Cybersecurity and Distributed Ledger Technology
• Machine Learning and Data Analytics

Background:

• The proliferation of intelligent energy storage devices generates vast amounts of detection data.
• Data sharing in distributed energy storage networks is crucial for collaborative control, comprehensive analysis, and enhanced intelligence.
• Existing data sharing methods face significant security challenges, primarily information leakage, hindering joint modeling and analysis.

Purpose of the Study:

• To propose a novel blockchain-based machine learning scheme for secure data sharing in distributed energy storage networks.
• To address the critical data security obstacles that impede effective data sharing and collaborative intelligence in these networks.
• To develop a system that ensures data security while facilitating joint modeling and analysis.

Main Methods:

• Formulated the data sharing problem as a machine learning problem incorporating secure federated learning.
• Developed innovative verification methods and consensus mechanisms to promote honest participation.
• Designed incentive mechanisms to ensure the sustainable and stable operation of the distributed system.
• Implemented the SFedChain scheme and conducted experiments on real-world datasets.

Main Results:

• The proposed SFedChain scheme demonstrates a promising approach to secure data sharing.
• Experimental results on real datasets validate the effectiveness of the implemented scheme under various settings.
• The system successfully integrates blockchain and federated learning for enhanced data security and network intelligence.

Conclusions:

• Blockchain-based machine learning offers a viable solution for secure data sharing in distributed energy storage networks.
• SFedChain effectively mitigates security risks associated with data sharing, paving the way for improved collaborative analysis and control.
• The developed system encourages honest participation and ensures stable operation, highlighting its potential for practical implementation.