MedShieldFL-a privacy-preserving hybrid federated learning framework for intelligent healthcare systems

Abstract

Recent advances in artificial intelligence have greatly increased the accuracy of computer-assisted diagnosis for serious conditions including brain tumours. However, concerns about data privacy, class imbalance, and the diversity of medical datasets limit the application of centralised deep learning models in healthcare. This article introduces MedShieldFL, a hybrid privacy-preserving federated learning architecture that enables secure and decentralised brain tumour classification across many medical institutions. The approach uses data augmentation techniques to reduce class imbalance and homomorphic encryption to safely aggregate model changes while safeguarding sensitive patient data. The basic model is a ResNet-18-based classifier that strikes the ideal balance between accuracy and speed. The test results for MedShieldFL show that it can accurately group data into 93% to 96% of the time. This approach improves performance by about 2% compared to traditional federated learning models and keeps data privacy safe enough. The framework makes sure that the extra work that encryption adds to real-world programs stays within acceptable limits. This keeps execution times fair. Medical picture evaluation with MedShieldFL is a useful and flexible technology that protects privacy. This makes it easier for current healthcare systems to use AI that is safe and works with other AI.

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