FlwrBC: Incentive Mechanism Design for Federated Learning by Using Blockchain

Abstract

The growth of information technology has resulted in a massive escalation of data and the demand for data exploration, particularly in the machine learning sector. However, machine learning raises concerns about data privacy because algorithms require large amounts of data to learn and make accurate predictions. Such data often contain personal information about individuals, and there is a risk that this information could be accessed or misused by unauthorized parties. It is crucial for organizations that use machine learning to prioritize personal data protection and ensure that appropriate safeguards are in place to prevent privacy breaches. Federated learning (FL) and blockchain technology are two increasingly popular approaches to distributed computing. Federated learning is a distributed machine-learning approach that trains machine-learning models on decentralized datasets without centralizing the data. Federated learning offers several benefits, including improved data privacy. Ensuring the benefit of clients in federated learning is vital for the success of this distributed machine learning approach, especially when combined with blockchain technology, as it offers a secure and transparent way to store and verify data. In this study, we propose a combination of federated learning and blockchain as a solution to some of the challenges faced by both approaches. By leveraging the decentralized nature of federated learning and the security and transparency of blockchain, our approach tends to overcome issues such as data privacy and trustworthiness of results. The evaluation results demonstrated that the proposed approach has many potential applications in various domains.