Integrated blockchain and federated learning for the cybersecurity of distributed energy resources

Blockchain and federated learning have emerged as complementary technologies for decentralized, privacy-preserving intelligent and secure management of sustainable distributed energy resources. This paper reviews the integration of blockchain technology with federated learning as an emerging approach to enhance the resilience of distributed energy resources against evolving cyber threats. First, a foundational four-layer cybersecurity framework is introduced for distributed energy resources’ device, firmware, cyber network and management application layers. Second, ten recent integrated blockchain and federated learning architectures are analyzed and categorized according to their functional layers and cyber security lifecycle stages. These architectures are systematically analyzed with respect to the following indices, to facilitate their selection for practical deployment: latency, security, scalability, throughput, energy consumption, complexity, and fault tolerance. Furthermore, critical challenges and emerging future research directions are also identified, such as quantum-safe multi-chain systems and quantum-resilient cryptography. Performance evaluation demonstrates that resource-efficient architectures optimize latency and energy consumption, and security-maximized architectures prioritize data protection at computational trade-offs. This review concludes that integrated blockchain and federated learning is a promising solution for secure, reliable and scalable next-generation smart grids. • Reviews the integration of blockchain with federated learning for smart grids. • Distributed energy resource layers and cybersecurity lifecycle stages. • Highlights strengths, limitations and trade-offs for the architectures. • Compares latency, security, scalability, throughput, energy consumption, etc. • Identifies critical challenges and outlines promising future research directions.