Research on Medical Image Classification Based on Improved FedAvg Algorithm

Federated learning technology has significant advantages in solving data silos and user privacy problems, but the traditional federal average algorithm (FedAvg) is ineffective in classifying and faces the risk of refactoring attacks when dealing with non-Independent and identically distributed (Non-IID) data, which is especially prominent since medical data involves sensitive personal health information. Therefore, optimizing FedAvg to adapt to Non-IID data distribution and enhancing privacy protection are urgent problems that need to be solved, and this paper investigates these two aspects. In order to enhance the classification performance of FedAvg under Non-IID distribution, this paper combines the optimized deep learning model SE-ResNet18-E with FedAvg to obtain the FedAvg(SE-ResNet18-E) algorithm. The algorithm takes advantage of the SE-ResNet18-E model in feature extraction and classification tasks, fully uses the data resources of each participant, and improves the classification performance of FedAvg under Non-IID distribution. In addition, the algorithm achieves high communication performance. Second, in order to enhance the security of federated learning in the medical domain, Threshold Paillier encryption is further introduced on top of FedAvg(SE-ResNet18-E) to form the Safe-FedAvg(SE-ResNet18-E) algorithm, which solves the threat of reconstruction attack and private key leakage in medical federated learning. After experimental validation, the Safe-FedAvg (SE-ResNet18-E) algorithm effectively improves the accuracy of disease classification and effectively protects the privacy and security of medical data, and enhances the trust of medical organizations participating in federated learning.