Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 4th International Conference on Signal Processing and Machine Learning

Series Vol. 52 , 27 March 2024


Open Access | Article

The application of federated learning in face recognition: A systematic investigation of the existing frameworks

Chuanzhi Xu * 1
1 University of Sydney

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 52, 21-30
Published 27 March 2024. © 27 March 2024 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Chuanzhi Xu. The application of federated learning in face recognition: A systematic investigation of the existing frameworks. ACE (2024) Vol. 52: 21-30. DOI: 10.54254/2755-2721/52/20241138.

Abstract

This paper presents a thorough examination of the recent progress made in applying federated learning to the field of face recognition. As face recognition technology continues to gain widespread adoption across various sectors, issues related to data privacy and efficiency have taken center stage. In response, federated learning, characterized by its decentralized machine learning approach, has emerged as a promising solution to tackle these pressing concerns. This review categorises the current federated learning frameworks for face recognition into four main purposes: Training Efficiency, Recognition Accuracy, Data Privacy, and Spoof Attack Detection. Each category is explored in-depth, highlighting the principles, structures, applicability, and advantages of the frameworks. The paper also delves into the challenges faced in the integration of federated learning and face recognition, such as high computational overhead, model inconsistency, and data heterogeneity. The review concludes with recommendations for future research directions, emphasising the need for model compression, asynchronous communication strategies, and techniques to address data heterogeneity. The findings underscore the potential and challenges of applying federated learning in face recognition, paving the way for more secure and efficient facial recognition systems.

Keywords

Federated Learning, Face Recognition, Data Privacy

References

1. Ding Y et al 2022 An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application ArXiv.org. https://doi.org/10.48550/arXiv.2206.13398

2. Aggarwal D Zhou J and Jain A K 2021 FedFace: Collaborative Learning of Face Recognition Model ArXiv:2104.03008 [Cs]. https://arxiv.org/abs/2104.03008

3. Zhuang W et al 2022 Federated Unsupervised Domain Adaptation for Face Recognition IEEE Xplore https://doi.org/10.1109/ICME52920.2022.9859587

4. Liu C-T et al 2022 FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition ArXiv.org. https://doi.org/10.48550/arXiv.2112.12496

5. Liu L Zhang Y Gao H Yu X and Cheng J 2022 FedFV: federated face verification via equivalent class embeddings Multimedia Systems https://doi.org/10.1007/s00530-022-00927-5

6. Shome D and Kar T 2021 FedAffect: Few-shot federated learning for facial expression recognition IEEE Xplore https://doi.org/10.1109/ICCVW54120.2021.00463

7. Shao R Perera P Yuen P C and Patel V M 2020 Federated Face Presentation Attack Detection ArXiv.org. https://doi.org/10.48550/arXiv.2005.14638

8. Chen Y Chen L Hong C and Wang X 2022 Federated Multitask Learning with Manifold Regularization for Face Spoof Attack Detection. Mathematical Problems in Engineering e7759410. https://doi.org/10.1155/2022/7759410

9. Zhu R Yin K Xiong H Tang H and Yin G 2021 Masked Face Detection Algorithm in the Dense Crowd Based on Federated Learning. Wireless Communications and Mobile Computing e8586016 https://doi.org/10.1155/2021/8586016

10. Niu Y and Deng W 2021 Federated Learning for Face Recognition with Gradient Correction ArXiv.org. https://doi.org/10.48550/arXiv.2112.07246

11. Shang E et al 2022 FedFR: Evaluation and Selection of Loss Functions for Federated Face Recognition 95–114 https://doi.org/10.1007/978-3-031-24383-7_6

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 4th International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-83558-349-4
ISBN (Online)
978-1-83558-350-0
Published Date
27 March 2024
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/52/20241138
Copyright
27 March 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated