A federated anti-forgetting representation method based on hybrid model architecture and gradient truncation

Hui WANG, Jie SUN, Tianyu WO, Xudong LIU, Suzhen PEI

PDF(409 KB)
PDF(409 KB)
Front. Comput. Sci. ›› 2025, Vol. 19 ›› Issue (6) : 196339. DOI: 10.1007/s11704-024-40557-w
Artificial Intelligence
LETTER

A federated anti-forgetting representation method based on hybrid model architecture and gradient truncation

Author information +
History +

Graphical abstract

Cite this article

Download citation ▾
Hui WANG, Jie SUN, Tianyu WO, Xudong LIU, Suzhen PEI. A federated anti-forgetting representation method based on hybrid model architecture and gradient truncation. Front. Comput. Sci., 2025, 19(6): 196339 https://doi.org/10.1007/s11704-024-40557-w

References

[1]
Liu F, Zheng Z, Shi Y, Tong Y, Zhang Y . A survey on federated learning: a perspective from multi-party computation. Frontiers of Computer Science, 2024, 18( 1): 181336
[2]
Wang H, Sun J, Wo T, Liu X. FED-3DA: a dynamic and personalized federated learning framework. In: Proceedings of 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2023, 1–5
[3]
Liu Z, Wang Y, Vaidya S, Ruehle F, Halverson J, Soljačić M, Hou T Y, Tegmark M. KAN: Kolmogorov-Arnold networks. 2024, arXiv preprint arXiv: 2404.19756
[4]
Lu C, Zhou Y, Bao F, Chen J, Li C, Zhu J. DPM-solver: a fast ODE solver for diffusion probabilistic model sampling in around 10 steps. In: Proceedings of the 36th International Conference on Neural Information Processing Systems. 2022, 418
[5]
Chen X, He K. Exploring simple Siamese representation learning. In: Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2021, 15745–15753
[6]
Ren L, Jiang L, Zhang W, Li C . Label distribution similarity-based noise correction for crowdsourcing. Frontiers of Computer Science, 2024, 18( 5): 185323
[7]
Li Q, Li G, Niu W, Cao Y, Chang L, Tan J, Guo L . Boosting imbalanced data learning with wiener process oversampling. Frontiers of Computer Science, 2017, 11( 5): 836–851
[8]
Chen T, Kornblith S, Norouzi M, Hinton G. A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th International Conference on Machine Learning. 2020, 1597–1607
[9]
Zhang F, Kuang K, Chen L, You Z, Shen T, Xiao J, Zhang Y, Wu C, Wu F, Zhuang Y, Li X . Federated unsupervised representation learning. Frontiers of Information Technology & Electronic Engineering, 2023, 24( 8): 1181–1193
[10]
Yoon J, Jeong W, Lee G, Yang E, Hwang S J. Federated continual learning with weighted inter-client transfer. In: Proceedings of the 38th International Conference on Machine Learning. 2021, 12073–12086

Acknowledgements

This work was supported by the National Science and Technology Major Project (2022ZD0120203).

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

RIGHTS & PERMISSIONS

2025 Higher Education Press
AI Summary AI Mindmap
PDF(409 KB)

Accesses

Citations

Detail

Sections
Recommended

/