Neuravolt: Blockchain-Enabled AI Model Marketplaces for Secure and Transparent AI Asset Trading

Abstract

Many machine learning models have been developed in a variety of industries, including healthcare, finance, transportation, and e-commerce, as a result of the quick development of artificial intelligence (AI). Through AI model marketplaces, these models are valuable digital assets that can be shared, reused, and made profitable. However, the majority of current AI model marketplaces operate on centralised infrastructures that present a number of difficulties, such as a lack of transparency, theft of intellectual property, unlawful redistribution, and unequal revenue distribution. Once models are uploaded to centralised platforms, developers frequently have little control over how their models are used or accessed. Blockchain technology, which offers decentralised trust, immutability, and transparent transaction management, has recently surfaced as a promising way to overcome these constraints. Building safe and open marketplaces for trading AI models is made possible by combining blockchain technology with decentralised storage systems like the Interplanetary File System (IPFS) and smart contracts. Verifiable ownership, tamper-proof model storage, automated payment settlements, and equitable developer compensation are all made possible by these technologies. This paper provides a thorough analysis of previous studies on decentralised digital asset trading systems and blockchain-enabled AI model marketplaces. The study examines various strategies put forth in current literature with an emphasis on their architecture, security features, scalability issues, and usefulness. Key issues like latency, blockchain transaction costs, storage efficiency, and system complexity are also identified in the review. In order to create scalable, secure, and decentralised AI model marketplaces that encourage openness, justice, and cooperation within the global AI ecosystem, the paper concludes by outlining possible research avenues.