mlsanityData to 14 Oct 2026

GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning

arXiv 2308.10279 ↗First posted 20 Aug 2023cs.LG cs.CR cs.DCcited by 18

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Abstract (arXiv)

Federated Learning (FL) is popular for its privacy-preserving and collaborative learning capabilities. Recently, personalized FL (pFL) has received attention for its ability to address statistical heterogeneity and achieve personalization in FL. However, from the perspective of feature extraction, most existing pFL methods only focus on extracting global or personalized feature information during local training, which fails to meet the collaborative learning and personalization goals of pFL. To address this, we propose a new pFL method, named GPFL, to simultaneously learn global and personalized feature information on each client. We conduct extensive experiments on six datasets in three statistically heterogeneous settings and show the superiority of GPFL over ten state-of-the-art methods regarding effectiveness, scalability, fairness, stability, and privacy. Besides, GPFL mitigates overfitting and outperforms the baselines by up to 8.99% in accuracy.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 55 of its 69 references that are in the map. First posted on arXiv 20 Aug 2023.

Builds on

55 of 69 references.

References · 55

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38 more
  1. 2018Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labelscited by 997
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 6; 2026: 5.

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  1. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
  2. 2025S2FGL: Spatial Spectral Federated Graph Learningcited by 7
  3. 2024Towards Layer-Wise Personalized Federated Learning: Adaptive Layer Disentanglement via Conflicting Gradientscited by 3
  4. 2024Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformationcited by 2
  5. 2024Optimizing Personalized Federated Learning through Adaptive Layer-Wise Learningcited by 2
  6. 2025Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learningcited by 2
  7. 2024Variational Bayes for Federated Continual Learningcited by 1
  8. 2026OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imagingcited by 1
  9. 2024Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneitycited by 0
  10. 2024Adaptive Guidance for Local Training in Heterogeneous Federated Learningcited by 0
  11. 2025Controlled privacy leakage propagation throughout overlapping grouped learningcited by 0
  12. 2025Whom to Trust? Adaptive Collaboration in Personalized Federated Learningcited by 0
6 more
  1. 2025Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspectivecited by 0
  2. 2025Dual-Distilled Heterogeneous Federated Learning with Adaptive Margins for Trainable Global Prototypescited by 0
  3. 2026Computation and Communication Efficient Federated Unlearning via On-server Gradient Conflict Mitigation and Expressioncited by 0
  4. 2026HEART-PFL: Stable Personalized Federated Learning under Heterogeneity with Hierarchical Directional Alignment and Adversarial Knowledge Transfercited by 0
  5. 2026Benchmark Evaluation of Federated Learning on Multi-organ Imagescited by 0
  6. 2026CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learningcited by 0