Improving histopathological image classification performance leveraging shared knowledge in a federated learning environment

Abstract

In today’s data-driven world, safeguarding sensitive data such as health records is es- sential to building a secure digital ecosystem. Federated Learning (FL) addresses these privacy concerns by enabling decentralized model training, where devices perform local training and share only model updates with a central server. This paper investigates the application of FL with shared learning in histopathological image classification using Mo- bileNetV2, VGG19, and DenseNet121 models to enhance convergence speed, accuracy, and scalability across diverse datasets. This research shows that MobileNetV2 achieved the largest reduction on the PatchCamelyon dataset (37 to 13). DenseNet121 outper- formed across all datasets, with reductions from 39 to 18 (PatchCamelyon), 35 to 18 (Breast Histopathology), 85 to 67 (Chaoyang), and 28 to 13 (Histopathology Imagery). VGG19, though less impactful, performed best on the Histopathology Imagery dataset (23 to 9). Furthermore, MobileNetV2 demonstrated a significant accuracy improvement on the highly imbalanced Chaoyang dataset, increasing from 69% to 82%. These findings highlight the real-world advantages of faster convergence and improved efficiency, par- ticularly in resource-constrained fields like healthcare. By leveraging shared knowledge across datasets, our approach enhances model generalization and robustness without cen- tralized data collection, paving the way for scalable, cost-effective, and privacy-preserving AI systems for medical image classification.