The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
FedFace: Collaborative Learning of Face Recognition Model
Open accessAbstract
DNN-based face recognition models require large centrally aggregated face datasets for training. However, due to the growing data privacy concerns and legal restrictions, accessing and sharing face datasets has become exceedingly difficult. We propose FedFace, a federated learning (FL) framework for collaborative learning of face recognition models in a privacy aware manner. FedFace utilizes the face images available on multiple clients to learn an accurate and generalizable face recognition model where the face images stored at each client are neither shared with other clients nor the central host and each client is a mobile device containing face images pertaining to only the owner of the device (one identity per client). Our experiments show the effectiveness of FedFace in enhancing the verification performance of pre-trained face recognition system on standard face verification benchmarks namely LFW, IJB-A and IJB-C.
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Privacy-preserving techniques like federated learning provide a solution to extract knowledge from distributed user data while preserving the identification-sensitive information.
“Privacy-preserving techniques like federated learning provide a solution to extract knowledge from distributed user data while preserving the identification-sensitive information [4, 9].”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [4]
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Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772
CosFace: Large Margin Cosine Loss for Deep Face Recognition
Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., & Liu, W. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00552 · Cited by 3,034
Self-citationFederated Learning With Differential Privacy: Algorithms and Performance Analysis
Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., & Vincent Poor, H. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2988575 · Cited by 2,400
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69250-1 · Cited by 1,646
Generative Models for Effective ML on Private, Decentralized Datasets
Augenstein, S., McMahan, H. B., Ramage, D., Ramaswamy, S., Kairouz, P., Chen, M., Mathews, R., & Arcas, B. A. Y. (2019) · arXiv · doi:10.48550/arxiv.1911.06679 · Cited by 103
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Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612
Metadata
- Authors
- Aggarwal, D., Zhou, J., & Jain, A. K.
- Affiliations
- Michigan State University (US)
- Venue
- 2021 IEEE International Joint Conference on Biometrics (IJCB)
- Publisher
- IEEE
- Published
- 2021-08-04
- Pages
- 1-8
- OpenAlex
- W3142093563
- Cited by
- 67
- References
- 6
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 24 · 2023: 13 · 2022: 7 · 2021: 4
- FWCI
- 5.54
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Joint Conference on Biometrics (IJCB) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, public-domain)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Office of Naval Research (N00014-20-1-2382, N00014-20, N00014)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- federated learning, face recognition, face verification, data privacy, mobile devices, cooperative learning
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