RL-Based Incentive Cooperative Data Learning Framework Over Blockchain in Healthcare Applications (RL-ICDL-BC)

Abstract

In recent years, significant strides in various domains have been fueled by the convergence of large-scale datasets and sophisticated machine learning algorithms. Nevertheless, the utilization of these datasets poses challenges, including privacy concerns, data ownership issues, and resource limitations. Cooperative data learning approaches have emerged as a solution, allowing multiple parties to collaboratively train machine learning models using their distributed data. While Federated Learning (FL) addresses the issue of privacy concerns, reluctance among data owners to share their data remains a challenge. It is imperative to provide incentives for participation in these cooperative learning settings to boost effectiveness and promote the widespread adoption of such approaches. This paper introduces an RL-ICDL-BC framework that seamlessly integrates principles of incentive design and cooperative learning, fostering effective collaboration among data owners. The framework's primary objective is to motivate and reward participants for contributing their models while simultaneously preserving privacy and ensuring fairness in the learning process. Experimental evaluations utilizing Covid-19 datasets and diverse collaborative learning scenarios demonstrate the effectiveness of the proposed framework. The results reveal that incentivizing cooperative data learning leads to increased participation rates, improved model performance, and enhanced fairness in the learning process. Despite the challenges posed by non-iid data, the experiments yield outstanding outcomes, showcasing a Covid19 virus detection accuracy rate of approximately 99%. This exceptional accuracy underscores the efficacy of our proposed approach in effectively detecting and mitigating the transmission of infectious diseases.