Blockchain for medical collaboration: A federated learning-based approach for multi-class respiratory disease classification

The scarcity and diversity of medical data have made it challenging to build an accurate global classification model in the healthcare sector. The prime reason is privacy concerns and legal obstacles which limit data-sharing scope among institutions in healthcare. On the other hand, data from a single source is hardly sufficient to develop a universal diagnosis model. While federated learning is a potential solution to privacy and data diversity concerns (allows distributed model training), an apt aggregation process for multi-class and heterogenous medical data is still at the outset. This study aims to propose a federated learning mechanism that can effectively learn from multi-class and heterogenous respiratory medical data. The proposed system trains and aggregates the local model by leveraging…