Aug 2026· Research on Biomedical Engineering· Vol 42· 0 citations· 43 references
TL;DR
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.
Abstract
Medical imaging has been transformed by Artificial Intelligence (AI) and Deep Learning (DL). Yet, multi-hospital deployment remains limited by patient privacy concerns, heterogeneous data distributions, and insufficient model interpretability, which affect regulatory approval and clinical trust. This study proposes a regulatory-grade Federated Learning (FL) framework for secure, interpretable, and generalizable collaborative medical imaging. The proposed framework integrates Slicing Window Adaptive Kalman Filtering (SWAKF) for image denoising, Structured Multi-Modal Autoencoder Attention Fusion (SMAAF) for feature representation, and adaptive federated aggregation to address non-IID data across hospitals. Patient privacy is preserved using secure aggregation, differential privacy, and encryption, while Grad-CAM, SHAP, and LIME provide model interpretability. The proposed framework outperformed Vision Transformer, AlexNet, FedAvg, and FedProx on Brain Tumor and Alzheimer's MRI datasets. It achieved 96.1% accuracy F1-score 96.1%, and 0.978 for Brain Tumor classification, and 94.8% accuracy and 0.968 for Alzheimer's classification. The framework also reduced calibration error, exhibited minimal encryption overhead, maintained robustness under noisy-label and non-IID conditions, and demonstrated statistically significant improvements p < 0.01 over baseline methods. 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, it improves diagnostic performance while supporting regulatory compliance and clinical trust, demonstrating strong potential for deployment in multi-hospital clinical environments.
Experimental results demonstrate that FLMMIF generates high-quality fusion results that effectively protect data privacy while achieving precise node-specific personalization.
Lei Meng, Shuangsong Ren, Jing Wang et al.· 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
A PFL framework, FedSCF, which models client heterogeneity at the parameter level, including a relative perturbation-based sensitivity evaluation is designed to identify critical parameters for personalized modeling, while the remaining parameters participate in cross-client sharing.
Mingjun Wei, Rongyang Xu, Qian Zhang et al.· Engineering Research Express· 0 citations
Continuous, remote patient monitoring is now practically possible thanks to the expanding
use of wearable and bedside sensors enabled by the Internet of Things (IoT). However, the
centralised aggregation of physiological data needed by traditional deep learning pipelines
presents significant privacy, legal, and bandwidth issues. In order to build a shared diagnostic
model across dispersed IoT healthcare nodes without sending raw patient data to a central
server, this study suggests a federated deep learning (FDL) system. The framework integrates
a FedAvg/FedProx aggregation scheme at the server with a lightweight hybrid convolutional
neural network and bidirectional long short-term memory (CNN-BiLSTM) architecture for
local physiological-signal feature extraction. Differential privacy (DP) noise injection and
secure aggregation are added to prevent information leakage from shared gradients. We
present a simulation-based evaluation intended to characterise the expected accuracy,
communication-efficiency, and privacy-utility trade-offs of the framework in comparison to
centralised and local-only baselines. We also describe the end-to-end system architecture, the
on-device training and communication protocol, and the privacy-accounting method. The
results show that the suggested federated strategy can reduce per-round communication
volume by an order of magnitude, eliminate the need to transmit raw sensor data, and
approach centralized-training accuracy within a narrow margin. We also examine how the
differential-privacy budget affects model utility and talk about unresolved issues with
adversarial robustness, device and network heterogeneity, and statistical heterogeneity (nonIID data). The suggested approach provides a workable blueprint for scalable, privacypreserving, and regulator-compliant AI-based healthcare monitoring at the network edge.
Firoza Sultana, Atiqur Rahman Laskar, Shamim Ahmed Shamim Khan Barbhuiya et al.· International Journal of Mod...· 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
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
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