Blockchain-Integrated Federated Learning Framework for Detecting False Data Injection Attacks in Power Systems With Homomorphic Encryption

False Data Injection Attacks (FDIAs) pose a substantial risk to the reliability and stability of Cyber-Physical Power Systems (CPPS). While federated learning (FL) has emerged as a promising approach to detect such attacks without exposing sensitive data, security concerns remain in FL, including untrusted central aggregators and potential malicious client updates. This research integrate a private Ethereum blockchain layer and homomorphic encryption into a secure FL framework for FDIA detection to verify model updates and authenticate participating nodes. We design smart contracts to immutably log model update hashes and enforce client authentication, enhancing traceability and tamper-resistance. A prototype implementation uses Ethereum smart contracts for model update verification and client identity management. We simulate the blockchain-integrated FL on a cyber-physical power system dataset using three detection models &#x2013; XGBoost, LSTM, and a Transformer &#x2013; and analyze the blockchain-induced latency and communication overhead under a specific network configuration. Results show that the blockchain layer has negligible impact on detection accuracy (global AUC <inline-formula> <tex-math notation="LaTeX">$\sim 0.94 \text {-}0.96$ </tex-math></inline-formula> across models) while introducing a moderate training time overhead (<inline-formula> <tex-math notation="LaTeX">$\sim 13- -40\%$ </tex-math></inline-formula> increase in training duration due to block confirmation delays). The proposed research demonstrates a viable approach to blockchain-aided federated learning for critical infrastructure security, combining data privacy, model integrity, and participant trust in a unified framework.