Leveraging Blockchain Based Federated Learning for Trustworthy 6G IoMT Image Analysis

Remote healthcare has been an important application of 6G and is increasingly attracting attention. In the field of medical image analysis, federated learning (FL) is widely considered for medical data collected by Internet of medical things (loMT) devices from different hospitals. In FL, communication overhead and local data valuation are two inevitable issues that urgently need to be addressed. The communication overhead between local clients and a central server can assist in enhancing the perception ability of FL. The data quality of local clients should be positively correlated with the contribution of their aggregation model. As such, this article discusses an efficient causal learning-based compression scheme of local data to reduce the amount of data for communication, namely, feature-heterogeneity-aware model, which will lower communication overhead without affecting the performance. Additionally, local client evaluation based on a model-driven and artificial intelligence (AI)-driven mode is analyzed respectively with the assistance of blockchain due to the data sensitivity of medical images in this article. As a result, the client weight during global aggregation can be adjusted adaptively according to their objective contribution. The simulations are made, and the results validate the effectiveness of proposed schemes.