Blockchain-Assisted Federated Learning to Secure Critical IoT Infrastructure

The expansion of Internet of Things (IoT) devices brings challenges of data security and privacy preservation in critical infrastructure. The proposed system combines blockchain technology with federated learning (FL) to secure IoT communications. It ensures decentralized model training with immutable and verifiable blockchain records. The study incorporates a lightweight FL model with a two-stage multitask head for binary and multiclass attack detection, enabling efficient deployment in constrained IoT. Federated learning effectively resolves privacy issues by facilitating cooperative model training across dispersed IoT nodes without revealing raw data. It guarantees collaboration based on a trust-aware mechanism that evaluates the reliability of clients and guides the robust aggregation. Blockchain ensures tamper-evident auditing of model updates and supports Byzantine-fault-tolerant (BFT) and Delegated proof-of-stake (DPoS) trust guarantees. Blockchain records cryptographic hashes of model modifications in a tamper-proof ledger, ensuring the legitimacy of the training process. Smart contracts enable the tamper-evident logging of model hashes, and global model convergence is ensured by federated averaging. Experimental tests demonstrate a secure and verifiable collaborative learning enabling model integrity in IoT networks. The study achieved a fast block generation time of 77.3ms, satisfactory model performance with 98% of training accuracy, and 0.992 F1-score alongside meaningful evolution of client trust values. The final testing accuracy of the FL model for the binary class detection is 98.1%. In a multi-class attack scenario, the FL model achieves a strong multi-class attack detection rate for dominant attack types.