International Journal on Science and Technology

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

Author(s) Sanjeev Kumar Pellikoduku
Country United States
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.
Keywords Federated Learning, Blockchain-Enhanced AI, Zero-Knowledge Proofs, Decentralized Computing, Privacy-Preserving Machine Learning
Field Computer
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-07
Cite This Decentralized AI Model Training Using Federated Learning and Blockchain in Cloud Environments - Sanjeev Kumar Pellikoduku - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2277
DOI https://doi.org/10.71097/IJSAT.v16.i1.2277
Short DOI https://doi.org/g87cv4
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