Skip to content
Log in

Similar papers

Open access Aug 2026

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.

Mingjun Wei, Rongyang Xu, Qian Zhang et al. · 0 citations
Preprint Jul 2026

FedProIn: Mitigating Client Drift for Learnable Prototypes in Federated Medical Imaging

This work proposes a prototype-based, influence-aware federated learning framework (FedProIn) that uses multiple learnable class prototypes to capture shared semantic structures across heterogeneous clients and introduces feature divergence loss and prototype contrastive loss to mitigate client drift by decomposing it into feature drift and prototype drift.

Harsh Kumar, T. Garg, V. Sundaresan · 0 citations
Aug 2026

STPFL: Stable teacher-guided personalized federated learning for heterogeneous multi-institution medical imaging

STPFL builds an EMA-based aggregated teacher to accumulate historical global knowledge and provide consistent guidance and improves global accuracy and F1-score, while personalized models improve by 4–25% and 4–32%, respectively.

R. Semwal, Imlimaong Aier, P. Varadwaj · 0 citations
Open access Jul 2026

FLMMIF: privacy-preserving federated multi-modal medical image fusion

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. · 0 citations
Open access Aug 2026

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.

Lei Yuan, Yaohua Luo, Mei Feng · 0 citations
Jul 2026

Federated Learning with Global Model Hint for Medical Image Object Detection.

This work proposes FedMHDet: Model Hint Federated Learning Detection Model, a novel federated learning detection framework that leverages multi-scale feature consistency as a global model hint to guide client models, thus mitigating the feature drift problem.

Zhenghua Xu, Gaoxi Zhou, Hexiang Zhang et al. · 0 citations