AdaptPFL: Unlocking Cross-Device Palmprint Recognition via Adaptive Personalized Federated Learning with Feature Decoupling

Zhang, Zirui; Guan, Donghai; Koç, Çetin Kaya; Wen, Jie; Zhu, Qi

doi:10.24963/ijcai.2025/787

AdaptPFL: Unlocking Cross-Device Palmprint Recognition via Adaptive Personalized Federated Learning with Feature Decoupling

Zirui Zhang, Donghai Guan, Çetin Kaya Koç, Jie Wen, Qi Zhu

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 7074-7082.

https://doi.org/10.24963/ijcai.2025/787

Keywords

Machine Learning: ML: Feature extraction, selection and dimensionality reduction
Machine Learning: ML: Federated learning
Computer Vision: CV: Biometrics, face, gesture and pose recognition
Machine Learning: ML: Deep learning architectures

Abstract

Contactless palmprint recognition has recently emerged as a promising biometric technology. However, traditional methods that require sharing user data introduce substantial security risks. While federated learning offers privacy-preserving solutions, it often compromises recognition accuracy due to feature distribution drift caused by external factors such as lighting and devices. To address this issue, we propose an adaptive personalized federated learning framework (AdaptPFL). The central innovation lies in decomposing palmprint features into identity-related and contextual-related components using a feature decoupling mechanism. This design isolates the influence of external environmental factors on identity recognition through de-entanglement. Furthermore, two adaptive aggregation strategies are introduced to correct client drift: (1) Intra-Local Adaptive Aggregation (ILAA), which addresses intra-client drift by adaptively combining the two decoupled feature types; (2) Global-Local Adaptive Aggregation (GLAA), which corrects inter-client drift by adaptively aggregating model parameters. Experimental results demonstrate that AdaptPFL achieves superior performance compared to existing state-of-the-art methods.

Zirui Zhang, Donghai Guan, Çetin Kaya Koç, Jie Wen, and Qi Zhu. 2025. AdaptPFL: Unlocking Cross-Device Palmprint Recognition via Adaptive Personalized Federated Learning with Feature Decoupling. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 7074–7082. https://doi.org/10.24963/ijcai.2025/787

@inproceedings{ijcai2025p787,
  title     = {AdaptPFL: Unlocking Cross-Device Palmprint Recognition via Adaptive Personalized Federated Learning with Feature Decoupling},
  author    = {Zhang, Zirui and Guan, Donghai and Koç, Çetin Kaya and Wen, Jie and Zhu, Qi},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {7074--7082},
  year      = {2025},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2025/787},
  url       = {https://doi.org/10.24963/ijcai.2025/787},
}

TY  - CPAPER
TI  - AdaptPFL: Unlocking Cross-Device Palmprint Recognition via Adaptive Personalized Federated Learning with Feature Decoupling
AU  - Zhang, Zirui
AU  - Guan, Donghai
AU  - Koç, Çetin Kaya
AU  - Wen, Jie
AU  - Zhu, Qi
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 7074
EP  - 7082
DO  - 10.24963/ijcai.2025/787
UR  - https://doi.org/10.24963/ijcai.2025/787
ER  -