Blockchain and Federated Learning Integration for Secure IoT and Cyber-Physical Systems
Deven Chawla, Dipen Chawla, Anurag Shrivastava, Myasar Mundher Adnan, B. Sireesha, Irfan Khan
Abstract
The proliferation of Internet of Things (IoT) and Cyber-Physical Systems (CPS) has ushered in an era of unprecedented data generation and automation. However, this interconnectivity introduces profound security and privacy challenges, particularly concerning the centralized aggregation of sensitive data for machine learning model training. Traditional cloud-centric approaches are vulnerable to single points of failure, data breaches, and privacy infringements. This paper investigates the synergistic integration of two transformative technologies-Blockchain and Federated Learning (FL)-as a robust framework to address these critical shortcomings. Federated Learning enables the collaborative training of machine learning models across distributed devices without centralizing raw data, thereby preserving data locality and privacy. Blockchain technology complements this by providing a decentralized, immutable, and transparent ledger to orchestrate the FL process securely. It facilitates trustworthy model aggregation, verifiable participant contribution, and resilient consensus mechanisms, mitigating risks such as model poisoning and malicious node attacks. The integration establishes a verifiable and auditable trail for all transactions within the FL lifecycle, from participant selection to global model updates. This research delineates the architectural principles of this integration, analyzes its efficacy in enhancing security and privacy for IoT and CPS, and discusses prevailing challenges and future research trajectories aimed at realizing a secure and trustworthy intelligent infrastructure.