Personalized Federated Learning with Progressive Local Training Strategy and Lightweight Classifier
Liu, Jianhao (China University of Petroleum)
Gong, Wenjuan (China University of Petroleum)
Fang, Ziyi (China University of Petroleum)
Gonzàlez, Jordi (Universitat Autònoma de Barcelona)
Rodrigues, Joel (Amazonas State University (Manaus, Brasil))

Fecha:	2025
Resumen:	Data heterogeneity poses a significant challenge in federated learning (FL), which has become a central focus of contemporary research in artificial intelligence. Personalized federated learning (pFL), a specialized branch of FL, seeks to address this issue by tailoring models to the unique data distributions of individual clients. Despite its potential, current pFL frameworks face critical limitations, particularly in handling client training discontinuity. When clients are unable to engage in every training round, the resulting models tend to diverge from their local knowledge, leading to catastrophic forgetting. Moreover, existing frameworks often separate the model from the local classifier used for personalization, keeping the classifier local for extended periods. This inherent characteristic of classifiers frequently leads to overfitting on local training data, thereby impairing the generalization capability of the local models. To tackle these challenges, we propose a novel personalized federated learning framework, PFPS-LWC (Personalized Federated Learning with a Progressive Local Training Strategy and a Lightweight Classifier). Our approach introduces local knowledge recall and employs regularized classifiers to mitigate the effects of local knowledge forgetting and enhance the generalization of the models. We evaluated the performance of PFPS-LWC under varying degrees of data heterogeneity using the CIFAR10 and CIFAR100 datasets. Our method outperformed the state-of-the-art approach by up to 4. 22% and consistently achieved the best performance across various heterogeneous environments, further demonstrating its effectiveness and robustness.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Federatedlearning ; Personalized federated learning ; Data heterogeneity ; Catastrophic forgetting
Publicado en:	Applied sciences (Basel), Vol. 15, Issue 5 (February 2025) , art. 2481, ISSN 2076-3417

DOI: 10.3390/app15052481

26 p, 7.9 MB

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