Privacy-preserving blood cancer detection via vision transformers and federated learning
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Abstract
Artificial Intelligence acts as a powerful tool to analyze medical images in smart healthcare. However, selecting a deep learning architecture is difficult in distributed computing environments due to privacy issues that prohibit centralized training. In this study, five architectures were considered for the evaluation of the performance of blood cancer image classification via federated learning (FL), where a baseline Convolutional Neural Network (CNN), ResNet50, DenseNet121, Xception, and a Vision Transformer (ViT) were selected. This setting allows the exploration of each architecture's performance in terms of image classification in a distributed, data-partitioned setting. These architectures were trained using the same federated learning framework. The experimental results show that such federated configurations offer competitive results compared with traditional methods used in centralized training of neural networks. According to our analysis, the best performance in accuracy, precision, recall, and F1-score was obtained with the ViT architecture, with a score of 0.98. Xception came second by obtaining accuracy and F1-score scores of 0.89, while ResNet50, CNN, and DenseNet121 achieved lower scores in the range of 0.82 - 0.86. The use of FL in conjunction with ViT yielded the highest score of accuracy and was more robust than the other FL methods.
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© 2026 The authors. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License.
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