Advancing medical data classification through federated learning and blockchain incentive mechanism: implications for modern software systems and applications

Abstract

The key issue of medical data is patient information sensitivity and dataset finiteness, which need to guarantee high-efficient training. Besides, the current convolutional neural network has a low image classification and poor robustness concerning antagonistic samples. A lack of scalability in healthcare federated learning and incentive mechanism hinders the attraction of ample high-quality datasets. This paper proposes a Federated Learning Incentive Mechanism for Medical Data Classification (FedIn-MC). It realizes a collaborative model training of multi-party medical institutions through the combination of federated learning and blockchain. There is a marked improvement to the model’s robustness through a combination of the distance loss function and the prototype loss regulation. In addition, this incentive mechanism of blockchain in the project is applied to calculate client contribution values and encourage healthcare institutions to active training model participation. Simulation results verify an accomplishment of a multi-party training. With regard to image classifications, this framework also has a higher classification accuracy and stronger robustness concerning invisible class samples.