Aug 2026· International Journal of Online and Biomedical Engineering (iJOE)· Vol 22· 0 citations· 19 references
TL;DR
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.
Abstract
Background: Gastrointestinal (GI) diseases, including colorectal cancer, gastric cancer, polyps, and inflammatory bowel disease, account for over three million deaths annually worldwide. Automated deep learning-based screening from endoscopic images has demonstrated strong diagnostic potential; however, cross-institutional collaboration is severely impaired by patient data privacy regulations, yielding under-powered models trained on single-site data. We propose FedGI-Screen, a novel federated learning (FL) framework for privacy-preserving multi-institutional GI disease screening. The system integrates three original contributions: (i) a Heterogeneity-aware Federated Aggregation (HFA) algorithm that weights client contributions by data quality and distributional divergence, addressing the critical non-IID challenge in heterogeneous hospital data; (ii) an Adaptive Differential Privacy (DP) module (Adaptive DP-SGD) with dynamic gradient clipping calibrated per communication round via a Rényi accountant, achieving tighter privacy-utility trade-offs; and (iii) an EfficientNet-B4 + Lightweight Vision Transformer (ViT) hybrid backbone with multi-scale endoscopic image preprocessing and GradCAM-based explainability for clinical transparency. Evaluated across five publicly available GI endoscopy datasets (Kvasir, HyperKvasir, GastroVision, KvasirCapsule, EDD 2020; N = 76,884 images) simulated across 8 federated clients under non-IID conditions, FedGI-Screen achieves 94.8% accuracy, 94.7% F1-score, and an AUC of 0.976— surpassing FedAvg by 7.5 and centralised training-without-federation by 1.7 percentage points in F1. Under DP (ε = 6, δ = 10-5), performance degrades by only 0.8%, demonstrating a strong privacy-utility balance. 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. The proposed HFA and Adaptive DP-SGD provide novel, reviewer-validated contributions that advance the state of the art in both federated medical imaging and gastroenterological AI.
ABSTRACT Skin cancer is among the most common malignant tumors worldwide, and early detection is essential to improve patient survival and recovery. Conventional AI‐based diagnostic approaches rely on centralized data, which raises security and privacy concerns. This paper presents an implementation of federated learning that addresses these challenges by enabling collaborative model training among simulated distributed client nodes while ensuring that raw patient data remains confidential. A convolutional neural network architecture, ResNet50, is employed at client nodes, with preprocessing steps including image augmentation, contrast enhancement, and lesion segmentation to improve feature extraction on the International Skin Imaging Collaboration (ISIC) dataset. Multiple aggregation algorithms are implemented at the central server, along with both vertical and horizontal federated learning settings. Their performance is evaluated using metrics such as precision, accuracy, recall, and F1‐score. In the horizontal federated learning setting, the FedNova aggregation approach outperformed other methods, achieving an accuracy of 76.45%, F1‐score of 0.73, and a recall of 0.72, demonstrating enhanced overall classification performance among the evaluated aggregation algorithms and stable convergence across clients. These results highlight the impact of federated learning settings and aggregation strategies on overall skin cancer detection performance.
B. Shrimali, Rebekah Geddam, H. Ghayvat et al.· Healthcare technology letter...· 0 citations
Kidney abnormalities, including cysts, tumors, and stones, are the most common renal disorders that can lead to severe complications such as chronic kidney disease or renal failure. Deep learning-based medical image analysis offers an effective approach for the accurate classification of kidney abnormalities, aiding the early diagnosis of renal disorders. However, its centralized training leads to inadequate privacy protection.
Considering the importance of ensuring individuals' data privacy, this study proposes a novel federated transfer learning framework for accurate classification of renal abnormalities using 12,446 kidney CT scan images and simultaneously preserves data privacy. CT scan images were preprocessed by resizing and normalization, followed by data augmentation techniques, including random rotations (±30°), horizontal flips, and color jitter, to address class imbalance and improve model generalization. Five pre-trained deep learning models such as MobileNetV2, EfficientNetV2-S, ResNet50, DenseNet121, and InceptionResNetV2 were trained across seven federated clients. Federated weighted averaging was employed for aggregation, and AES-256 encryption in CBC mode was applied to all model parameter transmissions between clients and the server.
MobileNetV2 achieved the best performance, attaining 99.48% accuracy, 99.29% precision, 99.32% recall, 99.3% F1-score, 0.9999 AUC-ROC, and log loss of 0.0247. Cross-client validation produced an average accuracy of 98.85% with a generalization gap of only −0.0063, indicating strong generalization across client datasets.
The proposed framework provides an effective balance between privacy preservation and communication efficiency, highlighting its potential for deployment in distributed clinical environments for kidney disease diagnosis.
Sai Sri Hemantha Konala, Srinivas Koppu· Frontiers in Artificial Inte...· 0 citations
The proposed FL framework provides a privacy-preserving, explainable, and computationally efficient solution for collaborative AI in medical imaging by combining adaptive federated learning, secure privacy mechanisms, and explainable AI techniques, demonstrating strong potential for deployment in multi-hospital clinical environments.
Chandra Shakher Tyagi, Partheeban Nagappan, T. R· Research on Biomedical Engin...· 0 citations
Experimental results on diabetic retinopathy and breast cancer pathology datasets demonstrate that PPFedKD outperforms baseline methods in classification accuracy, privacy protection, and communication efficiency, providing a secure and effective solution for medical image classification.
Lei Yuan, Yaohua Luo, Mei Feng· Expert Syst. J. Knowl. Eng.· 0 citations
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.
Varsha Jayakumar, Arathi Madhu, D. G· 2026 4th International Confe...· 0 citations
The proposed DP-SimAgg framework is a privacy-preserving federated learning framework that integrates similarity-weighted aggregation with a server-side differential privacy mechanism, and injects calibrated Gaussian noise at the central server, providing per-round privacy guarantees under the assumed sensitivity bound.
Muhammad Irfan Khan, E. Lehtonen, Joni Obradovic et al.· 0 citations
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