Blockchain-Secured Federated Deep Reinforcement Learning for Adaptive and Privacy-Preserving Industrial IoT Control

Abstract

Industrial Internet of Things (IIoT) systems require dynamical, safe, and competent control to address the dynamic industrial processes. Centralized Deep Reinforcement Learning (DRL) methods are however vulnerable to privacy, high communication overheads, and model tampering. In order to address these shortcomings, the present paper suggests a Blockchain-Secured Federated Deep Reinforcement Learning (BS-FDRL) system to privacy-guaranteed and resilient IIoT control. The suggested system has distributed edge agents, which locally train DRL models and exchange only encrypted policy updates through federated learning; this guarantees the privacy of data. A smart contract-based blockchain layer allows aggregation and safe validation of model parameters to be tampered with. Also, differential privacy is integrated to safeguard sensitive industrial data in the process of policy exchange. The framework is coded with Python 3.10, DDPG/PPO algorithms with PyTorch, Flower to coordinate federated, and Hyperledger Fabric to integrate with blockchains. Industrial control and anomaly detection experimental analyses show that performance improvements are significant. The increase in the cumulative reward of the proposed model is 18-22% and the convergence rate is about 28 times higher than the baseline approaches. Stability of control is improved to 91.3% and communication overhead is cut down by up to 25%. Moreover, the overall energy consumption is reduced by approximately 65%, which enhances the efficiency of the system. Security analysis indicates a 96% poisoning attack detection and 94% inference attack resistance. The system is robust and only 35% accuracy deteriorates in adverse conditions. The results prove that BS-FDRL offers scalable, secure, and efficient communication-based intelligent control in Industry 4.0 IIoT setups.