Developing a Decentralized AI Model Training Framework Using Blockchain Technology
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Abstract
This research addresses the critical challenges in traditional centralized AI model training, focusing on data privacy, security, and the risks associated with centralized data repositories. Integrating blockchain technology with AI model training aims to develop a decentralized framework that enhances data integrity and trustworthiness while mitigating vulnerabilities inherent in centralized systems. The objectives include designing and evaluating a blockchain-based infrastructure that supports security and collaboration. In the AI-model training, we leverage federated learning to enable data privacy-preserving mechanisms. The importance of this research lies in its ability to transfigure the current landscape of AI development by providing a robust solution that decentralizes data management, ensures transparency through immutable ledger technology, and automates secure interactions via smart contracts. Key contributions include conceptualizing and implementing a blockchain framework tailored for AI model training and incorporating decentralized data storage and smart contracts for task automation of federated learning for collaborative model development. Findings from experimental evaluations using the MNIST dataset demonstrate the framework's effectiveness in maintaining data privacy and enhancing security while achieving high client-produced accuracy (91%) and acknowledging challenges in generalizing model performance across heterogeneous datasets.
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