Decentralized Machine Learning on Blockchain: A Framework for Collaborative AI Model Training

Abstract

The integration of decentralized machine learning and blockchain technology presents a transformative approach to collaborative artificial intelligence (AI) model training. This paper proposes a framework that leverages blockchain's inherent features—transparency, data integrity, and fairness—to enhance the collaborative training of AI models across various stakeholders. In traditional centralized models, data privacy, trust issues, and potential biases often hinder collaboration and the quality of AI outputs. In contrast, a decentralized framework enables multiple parties to contribute to AI model training without the need for data sharing, maintaining privacy while ensuring that contributions are verifiable and auditable. This research highlights the technical architecture of the proposed framework, evaluates its potential benefits, and discusses challenges such as scalability, governance, and regulatory considerations. The findings demonstrate that a blockchain-based decentralized machine learning framework can significantly improve collaborative AI model training and foster innovation across industries.