pFedDS: parameter domain sensitivity based personalized federated learning

Conference Open access 2026

KT-pFL: A Personalized Federated Learning Model Based on Knowledge Transfer

. To address the issues of data privacy protection and the difficulty of federated learning meeting personalized needs in heterogeneous (non-IID) scenarios, this paper proposes a personalized federated learning model based on knowledge transfer, KT-pFL, and completes its experimental reproduction and verification. This...

Chen-Rui Li · Proceedings of the 4th Inter... · 0 citations

Preprint Aug 2026

Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning

Local Inference Guided Aggregation for Heterogeneous Training Environments to Yield Enhancement Through Agreement and Regularization (LIGHTYEAR), a federated learning framework that performs update selection in function space using an NTK-based agreement score to characterize predictive behavior and determine a persona...

Mirko Konstantin, S. Zachow, Anirban Mukhopadhyay · 0 citations

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. · Engineering Research Express · 0 citations

#machine learning Preprint Sep 2026

Similarity-Aware Personalized Federated Learning in Heterogeneous Environments

This work proposes SAPE-FL (Similarity-Aware Personalized Federated Learning), a novel personalization framework that anchors each client's model to both the global model and a similarity-weighted peer averaged model that mitigates negative transfer and enhances robustness in heterogeneous settings.

V. ArunKumarA, Sunil Gupta, Ngyuen Dang et al. · 0 citations

Preprint Aug 2026

Global Federated Learning Strategies for Building Efficient Personalized Models

Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously. Thi...

Seongyoon Kim · 0 citations

Conference Aug 2026

Privacy-Preserving Cross-Domain Data Mining Modeling Method Based on Federated Learning

This paper proposes a privacy-preserving modeling method for cross-domain data mining based on federated learning. Building on the FedAvg framework, the method integrates cross-domain data node modeling, heterogeneous feature semantic mapping, distribution-shift-aware aggregation, and dynamic gradient perturbation into...

Hao-Xuan Gao, Kang-Pei Li, Ya-Xuan Tian et al. · 2026 7th International Confe... · 0 citations