Blockchain-Integrated Federated Learning for Secure and Scalable AI Training

Abstract

The advent of artificial intelligence (AI) has underscored the need for effective data sharing methods that prioritize privacy and security. Federated learning (FL) emerges as a promising approach, allowing AI models to be trained on decentralized data across multiple devices while keeping the data localized. However, FL faces challenges related to data security, privacy, and scalability. This paper explores the integration of blockchain technology with federated learning, proposing a novel framework that enhances the security and scalability of AI training. By leveraging blockchain’s immutable ledger and consensus mechanisms, this framework addresses privacy concerns and fosters trust among participants. The synergistic combination of these technologies enables a robust environment for secure AI training across diverse devices, ultimately contributing to the advancement of decentralized AI systems.