Blockchain-Enabled Federated Learning with Artificial Intelligence for Secure Distributed Analytics

Abstract

The rapid proliferation of distributed computing environments and the increasing demand for privacy-preserving machine learning have created a critical need for secure, scalable, and trustworthy analytics frameworks. This paper proposes a novel Blockchain-Enabled Federated Learning framework integrated with Artificial Intelligence (BC-FL-AI) designed to address the fundamental challenges of data privacy, model integrity, and trustless coordination in distributed analytics. The proposed framework leverages the decentralized and immutable properties of blockchain technology to ensure transparent coordination among distributed participants, while federated learning enables collaborative model training without sharing raw data [1]. Artificial intelligence modules are embedded throughout the pipeline to enhance anomaly detection, adaptive aggregation, and dynamic resource allocation. Extensive experiments on benchmark datasets demonstrate that BC-FL-AI achieves 95.2% classification accuracy, reduces communication overhead by 65.3% compared to traditional methods, and attains a privacy preservation score of 93.7. The framework also demonstrates robust resilience against Byzantine attacks and Sybil threats [12]. These results establish the proposed system as a compelling solution for real-world secure distributed analytics applications including healthcare, finance, and smart infrastructure..