FedDP-PALD: A Privacy-Preserving Federated Latent Diffusion Framework with Prototype Aggregation for Medical Data Synthesis

Privacy-Preserving Cross-Domain Score Fusion for Distributed Public-Service Data: A Federated Architecture and Empirical Benchmark

Privacy-Preserving Heterogeneous Multi-LLM Federated Inference for Cognitive Diagnosis

Significant challenges remain in AI-driven educational systems in balancing privacy preservation with accurate cognitive diagnosis. To overcome this, we propose a federated inference framework in which several commercial LLM APIs collaborate without requiring access to raw student data or proprietary model internals. Using multiple federated entities, such as LLaMA-3.3-70B, GPT-4o-mini, and Claude-3-Haiku, our framework builds upon a heterogeneous multi-LLM architecture. The predictions generated by these entities are combined with epsilon-local differential privacy by adding Laplace noise locally to each entity's prediction output before aggregation, while residual-based aggregation mitigates model heterogeneity. Our approach is predicated on an honest-but-curious trust paradigm in which API providers are presumed not to abuse submitted queries, and our differential privacy mechanism shields the published diagnostic results from external inference. We conduct rigorous privacy-utility analysis showing strong privacy guarantees with minimal accuracy loss, and extensive real-world evaluations across three educational benchmarks confirm the framework's practical usability and cross-domain generalizability.

Regulatory-orientedDeep federated learning framework for multi-hospital medical imaging: privacy-preserving, explainable, and generalizable diagnosis

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.

FedSCF: sensitivity-aware collaborative fusion for personalized federated learning in medical image classification

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.

PPFedKD: Privacy‐Preserving Federated Learning and Adaptive Contrastive Distillation for Medical Image Diagnosis

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.

Hybrid Differentially Private Conditional Diffusion for Synthetic EHR Generation in Medical IoT Environments

Privacy-preserving synthesis of electronic health records is difficult because clinical data combine continuous measurements, categorical diagnoses, class imbalance, missingness, and multiple trust boundaries. PrivMedSynth is presented as a conditional diffusion framework for mixed type tabular health records in Medical Internet of Things edge cloud environments. The framework applies a local randomizer before transmission and trains a conditional diffusion model with differentially private stochastic gradient descent in the cloud. Continuous features are perturbed with a calibrated Gaussian mechanism, categorical variables are protected with generalized randomized response, and clinical conditions are used as explicit generation controls. Rényi differential privacy accounts for repeated cloud updates and is converted to an approximate differential privacy guarantee. The revised formulation distinguishes the formal privacy guarantee from empirical membership inference performance and makes the privacy unit, adjacency relation, modality budget, and noise parameterization explicit. The experiments report a total variation distance of 0.041, a downstream AUROC of 0.884, and a membership inference AUROC of 0.503 on MIMIC III. These utility and attack results remain conditional on the stated preprocessing and require reproduction under the corrected accounting before being interpreted as a final end to end guarantee. The framework provides a principled basis for studying the privacy and utility tradeoff in distributed synthetic clinical data generation.