Jul 2026· International Conference on Information and Communicatiaon Technology· pp. 1-6· 0 citations· 16 references
Abstract
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.
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
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.
Guda Madhu, Nirmalajyothi Narisetty· International journal of com...· 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
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
Experimental results support that the proposed framework has succeeded in providing better adversarial robustness while preserving data privacy, which is acceptable in cloud-IoT environments for secure medical image analysis.
Vijayalakshmi MM, Neelam Malayadri· International journal of com...· 0 citations
With the rapid adoption of cloud computing for healthcare data storage, ensuring the privacy and security of sensitive medical images has become a critical challenge. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), offer new possibilities for enhancing encryption and data protection without compromising image quality or diagnostic value. This research aims to develop a state-of-the-art, CNN-based privacy-preserving framework that leverages feature extraction and adaptive encryption methods to securely manage medical imaging data in cloud environments, aligning with emerging trends in AI-driven healthcare security and regulatory compliance. With the increasing reliance on cloud-based platforms for storing and sharing medical imaging data, privacy and security concerns have become paramount. Medical images such as CT scans, MRI, and X-rays contain sensitive patient information that must be protected from unauthorized access while ensuring usability for clinical diagnosis. This research proposes a Deep Learning-Enabled Privacy-Preserving Framework that integrates advanced convolutional neural network (CNN) architectures with adaptive encryption techniques to enhance the confidentiality of medical images stored in cloud environments. The framework extracts essential features from images while applying encryption algorithms that maintain data integrity and diagnostic value. Experimental results on diverse medical image datasets demonstrate the efficiency, robustness, and scalability of the proposed approach, making it suitable for modern healthcare applications where secure, cloud-based data management is essential. The proposed method aligns with current trends in artificial intelligence, cybersecurity, and regulatory standards for medical data protection.
Mekala Pooja, Sameer Bhondve, Dr. Bharti A. Dixit· Journal of Intelligent Decis...· 0 citations
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