Decentralized AI Model Training Using Federated Learning and Blockchain in Cloud Environments

Abstract

This article presents a novel framework for decentralized artificial intelligence model training that combines federated learning with blockchain technology in cloud environments. By integrating these cutting-edge technologies, the article addresses critical challenges in collaborative AI development, including data privacy, secure model sharing, and participant incentivization. The article framework leverages Zero Knowledge Proofs (ZKPs) for enhanced privacy guarantees while utilizing blockchain-based smart contracts to ensure transparent and automated governance of the training process. The implementation demonstrates significant improvements in data transfer efficiency, privacy preservation, system reliability, and participant diversity compared to traditional centralized approaches. The results validate the effectiveness of combining federated learning with blockchain technology for secure, scalable, and efficient distributed AI model training.