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AdaFedFR: Federated Face Recognition with Adaptive Inter-Class Representation Learning (2405.13467v1)

Published 22 May 2024 in cs.CV

Abstract: With the growing attention on data privacy and communication security in face recognition applications, federated learning has been introduced to learn a face recognition model with decentralized datasets in a privacy-preserving manner. However, existing works still face challenges such as unsatisfying performance and additional communication costs, limiting their applicability in real-world scenarios. In this paper, we propose a simple yet effective federated face recognition framework called AdaFedFR, by devising an adaptive inter-class representation learning algorithm to enhance the generalization of the generic face model and the efficiency of federated training under strict privacy-preservation. In particular, our work delicately utilizes feature representations of public identities as learnable negative knowledge to optimize the local objective within the feature space, which further encourages the local model to learn powerful representations and optimize personalized models for clients. Experimental results demonstrate that our method outperforms previous approaches on several prevalent face recognition benchmarks within less than 3 communication rounds, which shows communication-friendly and great efficiency.

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References (39)
  1. Fedface: Collaborative learning of face recognition model. In 2021 IEEE International Joint Conference on Biometrics (IJCB), pages 1–8. IEEE, 2021.
  2. Partial fc: Training 10 million identities on a single machine. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1445–1449, 2021.
  3. Vggface2: A dataset for recognising faces across pose and age. In 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018), pages 67–74. IEEE, 2018.
  4. Sub-center arcface: Boosting face recognition by large-scale noisy web faces. In European Conference on Computer Vision, pages 741–757. Springer, 2020.
  5. Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4690–4699, 2019.
  6. Variational prototype learning for deep face recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11906–11915, 2021.
  7. Marginal loss for deep face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 60–68, 2017.
  8. Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. In European conference on computer vision. Springer, 2016.
  9. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  10. Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. In Workshop on faces in’Real-Life’Images: detection, alignment, and recognition, 2008.
  11. Curricularface: adaptive curriculum learning loss for deep face recognition. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5901–5910, 2020.
  12. Scaffold: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning, pages 5132–5143. PMLR, 2020.
  13. Model-contrastive federated learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10713–10722, 2021.
  14. A survey on federated learning systems: vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering, 2021.
  15. Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems, 2:429–450, 2020.
  16. Fedfr: Joint optimization federated framework for generic and personalized face recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 1656–1664, 2022.
  17. Sphereface: Deep hypersphere embedding for face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 212–220, 2017.
  18. Large-margin softmax loss for convolutional neural networks. arXiv preprint arXiv:1612.02295, 2016.
  19. Deep face recognition: A survey. In 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI), pages 471–478. IEEE, 2018.
  20. Iarpa janus benchmark-c: Face dataset and protocol. In 2018 international conference on biometrics (ICB), pages 158–165. IEEE, 2018.
  21. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273–1282. PMLR, 2017.
  22. Improving federated learning face recognition via privacy-agnostic clusters. arXiv preprint arXiv:2201.12467, 2022.
  23. Agedb: the first manually collected, in-the-wild age database. In proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 51–59, 2017.
  24. Level playing field for million scale face recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7044–7053, 2017.
  25. Federated learning for face recognition with gradient correction. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 1999–2007, 2022.
  26. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 815–823, 2015.
  27. Frontal to profile face verification in the wild. In 2016 IEEE winter conference on applications of computer vision (WACV), pages 1–9. IEEE, 2016.
  28. Kihyuk Sohn. Improved deep metric learning with multi-class n-pair loss objective. Advances in neural information processing systems, 29, 2016.
  29. Deep learning face representation by joint identification-verification. Advances in neural information processing systems, 27, 2014.
  30. Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1701–1708, 2014.
  31. Ser-fiq: Unsupervised estimation of face image quality based on stochastic embedding robustness. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5651–5660, 2020.
  32. Paul Voigt and Axel Von dem Bussche. The eu general data protection regulation (gdpr). A Practical Guide, 1st Ed., Cham: Springer International Publishing, 10(3152676):10–5555, 2017.
  33. Additive margin softmax for face verification. IEEE Signal Processing Letters, 25(7):926–930, 2018.
  34. Cosface: Large margin cosine loss for deep face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5265–5274, 2018.
  35. Federated learning with differential privacy: Algorithms and performance analysis. IEEE Transactions on Information Forensics and Security, 15:3454–3469, 2020.
  36. Sphereface2: Binary classification is all you need for deep face recognition. arXiv preprint arXiv:2108.01513, 2021.
  37. Iarpa janus benchmark-b face dataset. In proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 90–98, 2017.
  38. Federated learning with only positive labels. In International Conference on Machine Learning, pages 10946–10956. PMLR, 2020.
  39. Webface260m: A benchmark unveiling the power of million-scale deep face recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10492–10502, 2021.

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