Blockchain-Enabled Federated Learning for Privacy-Preserving AI

Abstract

Federated Learning (FL) is a decentralized collaborative AI training paradigm that maintains privacy of the data. FL is still susceptible to security attacks, malicious clients, and model integrity issues. To mitigate these issues, we introduce a Blockchain-Enabled Federated Learning (BFL) system that incorporates decentralized ledger technology to provide tamper-evident model aggregation, transparent client engagement, and verifiable updates. The suggested BFL framework uses smart contracts to enable automated trust management, zero-knowledge proofs (ZKPs) to facilitate privacy-enhanced authentication, and an incentive mechanism based on tokenized rewards to promote honest engagement. We also propose an adaptive consensus protocol that maximizes blockchain overhead while preserving high scalability for real-world applications like cybersecurity, healthcare, and Industrial IoT (IIoT). Experimental results on benchmark datasets show that BFL dramatically improves model robustness against data poisoning and adversarial attacks with a 15-25% improvement in attack resilience over state-of-the-art FL methods. Our work presents a complete blueprint for secure, privacy-preserving AI and establishes a foundation for the next generation of decentralized intelligence.