Post-Quantum Secure Blockchain-Based Federated Learning Framework for Enhancing Smart Grid Security

Emerging technologies have accelerated the digitalization of smart grids, improving demand-side management, sustainability, and operational efficiency. The attack surface is widened by this interconnection, though, leaving vital smart grid data and systems vulnerable to online attacks. Single points of failure, privacy violations, and a lack of robustness against sophisticated attacks persist in centralized data processing. Traditional cryptographic techniques are further threatened by the development of quantum computing, which raises significant security risks for smart grids. With a focus on post-quantum cryptography (PQC) resilience, this study examines 206 peer-reviewed research articles on blockchain-based federated learning (BFL) in smart grids that were published between January 2023 and July 2025. It assesses the advantages, limitations, and compromises of the current BFL models in this field. The paper suggests a unique post-quantum secure BFL (PQS-BFL) framework that integrates federated learning (FL), lightweight PQC protocols, and a scalable blockchain architecture to solve the vulnerabilities that have been uncovered. This design enables decentralized, private, and impenetrable cooperation among grid nodes. The results demonstrate that the system mitigates quantum-resilient attacks and inference threats while improving data integrity, key management, and secure model aggregation. A path for creating safe, scalable PQS-BFL solutions for upcoming smart energy systems is provided in the paper's conclusion, along with an overview of the main research issues. This study shows that using PQC, blockchain, and FL to secure next-generation smart grids is both feasible and important.