Blockchain-Integrated Federated Learning for IoT-Based Smart Applications
S. Balakrishnan, Syed Shahul Hameed M, R. Kumar, S. Simonthomas · 2025
Blockchain-integrated federated learning (BIFL) emerges as a promising solution to address the intricate challenges surrounding privacy, security, and data integrity within the context of IoT-based smart applications. Federated learning addresses these issues by enabling decentralized model training, where multiple devices collaboratively improve a shared model without exchanging raw data. Despite its advantages, federated learning encounters several challenges, including issues of trust, data integrity, and the secure aggregation of model updates. In this chapter, we introduce a comprehensive framework that combines blockchain technology with federated learning methods to create a secure and efficient learning environment for distributed IoT devices. By leveraging blockchain, BIFL guarantees immutable and transparent records of model updates, transactions, and participant contributions, which helps build trust and accountability within the decentralized learning ecosystem. Furthermore, BIFL utilizes cryptographic techniques to maintain user privacy and data confidentiality throughout the federated learning process. Through a series of extensive simulations and real-world experiments, we demonstrate the effectiveness, scalability, and robustness of the proposed BIFL framework in improving the reliability and trustworthiness of IoT-based smart applications. Our results highlight the potential of BIFL to transform collaborative learning in IoT environments, leading to more secure, privacy-preserving, and resilient smart systems.