Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 1411-1416· 0 citations· 10 references
Abstract
Deep learning based medical diagnosis systems are often hindered by privacy restrictions on patients’ health data. In this paper, we propose a privacy preserving decentralized algorithm for multi-label classification of chest diseases using Federated Learning (FL). We experimented on 26,218 images sampled from NIH Chest X-ray dataset with a ResNeSt-50 deep neural network to classify 8 thoracic abnormalities. ResNeSt-50 incorporates Split-Attention mechanisms, which improve multiscale feature extraction for chest disease classification. As chest x-rays are low contrast images by nature, we applied Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing to input images. The experimental results show that our proposed FL method obtains a competitive accuracy score of 7.04/8.0 (88.01%) compared to the non-decentralized baseline with the added benefits of FL, such as reduction in validation loss. This work aims to serve as a baseline for future work in FL for chest imaging.
Blation studies confirm that federated hybrid learning with AFA and SMOTE outperforms standalone SVM, standalone KNN, and centralized hybrid baselines for all evaluation metrics, proving the clinical viability of the proposed framework for distributed hospital networks and telemedicine platforms.
Syed Riyazul Haq, K. Lakshmanna· Frontiers in Digital Health· 0 citations
FedGI-Screen demonstrates that privacy-preserving FL can match or exceed the performance of centralised models for GI disease screening, while maintaining rigorous data confidentiality compliance with GDPR and HIPAA.
S. Nithiya, S. Murugaanandam, K. Sornalakshmi et al.· International Journal of Onl...· 0 citations
An implementation of federated learning is presented that addresses challenges of collaborative model training among simulated distributed client nodes while ensuring that raw patient data remains confidential by enabling collaborative model training among simulated distributed client nodes while ensuring that raw pati...
B. Shrimali, Rebekah Geddam, H. Ghayvat et al.· Healthcare technology letter...· 0 citations
The federated approach provides a workable route toward privacy-compliant multi-institutional cardiac imaging analysis by achieving competitive accuracy while upholding formal differential privacy commitments, as confirmed by experimental results.
Chaithanya L, Veena K· International Research Journ...· 0 citations
Findings indicate that integrating compression and quantization into FedProx achieves substantial communication efficiency and faster convergence with only minor performance trade-offs, offering a viable option for resource-constrained edge deployments.
A hybrid MobileNetV2–Vision Transformer (ViT) framework for multi-label classification of CXR images into 14 disease categories on NIH CXR14 dataset is introduced, which adaptively optimizes key hyperparameters of the MobileNetV2–ViT framework to achieve improved accuracy, faster convergence, and enhanced computational...
R. Raj, Pavan M. P. Kumar, K. N. Manjunath et al.· Scientific Reports· 0 citations
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