Federated distillation and blockchain empowered secure knowledge sharing for Internet of medical Things

With the development of Internet of Things (IoT) and Artificial Intelligence (AI) technologies, smart services have penetrated into every aspect of our daily lives, including the medical treatment and healthcare fields. However, due to security and privacy issues, medical data cannot be easily shared, which may lead to the situation of the so-called data silos. Challenges in existing approaches when building medical data sharing models can be summarized as: i) It is very challenging to ensure that the privacy of the medical data is protected and to identify the ownership of medical data; ii) Their models always result in poor performance or have the problem of excessive communication overhead due to the large amount of model parameters; iii) The current scenario of combining federated learning and blockchain generally ignores the load on nodes, which may easily lead to a lack of efficiency and fairness during the consensus process. In this study, we propose a Federated Distillation and Blockchain empowered Secure Knowledge Sharing (FDBC-SKS) model, which transforms the data sharing problem into a collaborative model knowledge sharing problem, aiming to provide a lightweight distributed deep learning framework in Internet of Medical Things (IoMT) environments. A peer-to-peer federated distillation mechanism is designed to enable a decentralized federated learning with better model flexibility and less communication consumption, based on the better knowledge utilization from each local model. A reinforcement learning enhanced consensus mechanism for blockchain is devised to improve the model convergence performance, alleviate the problem of low computational efficiency, while enhancing the fairness in terms of node load balancing during the node selection and block generation process. Experiment and evaluation results based on two realworld datasets demonstrate the usefulness of our proposed model toward secure and effective data sharing in IoMT oriented smart application development compared with other similar methods.