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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 …