Collaborative Algorithm for User Trust and Data Security Based on Blockchain and Machine Learning

Abstract

Machine learning has achieved remarkable results in numerous fields, demonstrating strong momentum and promising prospects for future development. However, machine learning is facing issues related to data security. User data contains a vast amount of sensitive personal information, and once privacy is breached, users may not only suffer from harassment but also face threats to their lives and property security. As a result, users’ willingness and trust in sharing local raw data are gradually decreasing. In response to this situation, federated learning technology has emerged, which enables efficient training of decentralized data through distributed machine learning methods while protecting users’ data privacy. Traditional federated learning systems suffer from issues such as single points of failure and lack of trust. Blockchain, as a decentralized, traceable, and tamper-resistant distributed ledger technology, provides a new solution for federated learning. It records every update of the global model, verifies and tracks local updates, and is equipped with a fair incentive mechanism. Based on these ideas, this paper proposes a federated learning framework combined with blockchain, aiming to address data security issues in federated learning.