Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
The suggested framework serves as an accurate, scalable, and privacy-preserving approach for distributed medical imaging and aggregates only the model parameters at the central server rather than the raw medical data.
Abstract
One of the most frequent urological issues that calls for accurate, timely diagnosis and appropriate treatment is renal stone disease, and CT is very helpful in diagnosing renal stones. Methods of deep learning possess effective automated detection potential. However, most prevailing methods involve centralized training, raising privacy and multi-institutional collaboration concerns. In response to these challenges, this research presents FedStoneNet-Hybrid, a federated learning framework designed to protect the privacy of kidney stone detection on distributed CT data using a hybrid CNN and transformer. Under the proposed scheme, each institution will locally train its hybrid model, where CNN layers will capture a fine-grained spatial representation and transformer modules will capture the global contextual relationship of pollution data. The Federated Averaging algorithm aggregates only the model parameters at the central server rather than the raw medical data. Thus, safe and scalable collaborative learning can be enabled. As observed from the experimental results, the proposed model achieves good performance with an accuracy of 97.2%, sensitivity of 96.8%, specificity of 96.9%, F1 score of 0.96.85, and AUC of 0.991. The model shows solid calibration with a low Expected Calibration Error and Brier Score. Based on these findings, the suggested framework serves as an accurate, scalable, and privacy-preserving approach for distributed medical imaging.
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
Brain tumor detection from multi-institutional MRI datasets faces two compounding challenges: segmenting heterogeneous glioma sub-regions, and privacy regulations (HIPAA, GDPR) that prevent data centralization. This paper presents AdaFed-BrainGNN, an adaptive federated learning framework extending BrainGNN-Hybrid with three innovations: (1) AdaFedAvg — adaptive client-weighting aggregation via composite quality scores; (2) formal (ε, δ)-differential privacy via DP-SGD with Rényi DP (RDP) accounting; and (3) structured gradient sparsification that reduces communication by 73.4%. Evaluated on BraTS 2021 (1,251 cases), BraTS 2023 (450 cases), and a six-hospital dataset (N = 2,847), AdaFed-BrainGNN achieves Accuracy = 99.14%, F1 = 98.84%, AUC = 0.997, and ε = 2.31 (δ = 10⁻⁵) after 100 federation rounds, with AWS SageMaker inference at 38 ms per volume
Anoop Kumar, J. Shekhawat· JOURNAL OF MECHANICS OF CONT...· 0 citations
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
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
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
Federated learning (FL) enables collaborative medical image analysis without centralising sensitive data, making it highly suitable for privacy-critical applications such as brain tumour detection from magnetic resonance imaging (MRI). However, conventional FL frameworks remain vulnerable to parameter-level information leakage through shared model updates, which may expose sensitive information about the underlying medical data. This study proposes a privacy-preserving FL framework that integrates Cheon-Kim-Kim-Song (CKKS)-based homomorphic encryption (HE) for secure aggregation with an Xception convolutional neural network (CNN) for multi-class brain tumour classification. In the proposed framework, local models are trained in plaintext on distributed clients, while the resulting model parameters are encrypted before transmission and aggregation. This approach ensures that the central server performs aggregation without gaining access to plaintext model updates, thereby improving confidentiality and reducing the risk of privacy breaches. Experiments conducted on a four-class brain MRI dataset demonstrate that the proposed approach achieves a global classification accuracy of 97.87%, with less than 1% performance degradation compared to conventional non-encrypted FL. Furthermore, a comprehensive analysis of encryption, decryption, and federated communication round latency is performed to evaluate the computational overhead introduced by CKKS-based HE. The experimental findings confirm the practical feasibility of integrating HE-assisted secure aggregation with deep learning-based FL systems for privacy-preserving medical imaging applications, while maintaining high diagnostic performance and reliable collaborative model training.
Anik Sen, Swee-Huay Heng, Shing-Chiang Tan· International Conference on...· 0 citations
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