Decentralized Machine Learning on Blockchain: A Review of Recent Developments

Abstract

Decentralized machine learning (DML) is a new paradigm in artificial intelligence (AI) that combines the power of distributed computing and blockchain technology to enable secure and privacy-preserving machine learning. In DML, multiple devices or nodes collaborate to train a machine learning model without sharing their data, thereby enhancing data privacy and security. This research paper provides a comprehensive review of recent developments in DML on the blockchain, including its applications, challenges, and potential solutions. The paper analyzes relevant literature and case studies to highlight the advantages and limitations of DML on the blockchain. The study looks at the many consensus techniques used in DML and how they affect system performance, including proof-of-work, proof-of-stake, and proof-of-authority. The function of smart contracts in DML and how they might improve the system's security and transparency are also discussed in the paper. The paper also covers DML on the blockchain's difficulties and potential solutions, including scalability, interoperability, and privacy issues. According to the study's results, DML on the blockchain has the power to change the AI industry by providing safe and private machine learning. To address the technological and nontechnical problems, however, it also requires additional research and development. To fully realize the potential of DML on the blockchain, the study emphasizes the necessity of a coordinated effort by researchers, developers, and policymakers.