mlsanityData to 7 Oct 2026

FedFace: Collaborative Learning of Face Recognition Model

arXiv 2104.03008 ↗First posted 7 Apr 2021cs.CVcited by 26

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Abstract (arXiv)

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.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 18 of its 36 references that are in the map. First posted on arXiv 7 Apr 2021.

Builds on

18 of 36 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  6. 2018CosFace: Large Margin Cosine Loss for Deep Face Recognitioncited by 968
  7. 2019Towards Federated Learning at Scale: System Designcited by 826
  8. 2016MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognitioncited by 621
  9. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  10. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  11. 2014Learning Face Representation from Scratchcited by 451
  12. 2019Agnostic Federated Learningcited by 451
6 more
  1. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  2. 2019Federated Learning for Emoji Prediction in a Mobile Keyboardcited by 115
  3. 2019Generative Models for Effective ML on Private, Decentralized Datasetscited by 85
  4. 2020Federated Learning with Only Positive Labelscited by 55
  5. 2019Federated Learning Of Out-Of-Vocabulary Wordscited by 51
  6. 2020Federated Face Presentation Attack Detectioncited by 1

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Citing papers per year: 2021: 8; 2022: 4; 2023: 4; 2024: 10.

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  1. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  2. 2021Trustworthy AI: From Principles to Practicescited by 206
  3. 2021FedProc: Prototypical Contrastive Federated Learning on Non-IID datacited by 80
  4. 2021Biometrics: Trust, but Verifycited by 39
  5. 2021Federated Learning for Face Recognition with Gradient Correctioncited by 24
  6. 2022DYNAFED: Tackling Client Data Heterogeneity with Global Dynamicscited by 22
  7. 2021Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Settingcited by 19
  8. 2022Improving Federated Learning Face Recognition via Privacy-Agnostic Clusterscited by 18
  9. 2021FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognitioncited by 16
  10. 2023ProtoFL: Unsupervised Federated Learning via Prototypical Distillationcited by 11
  11. 2022FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoderscited by 6
  12. 2024Unlocking Visual Secrets: Inverting Features with Diffusion Priors for Image Reconstructioncited by 5
14 more
  1. 2023Federated Learning on Non-iid Data via Local and Global Distillationcited by 4
  2. 2021Federated learning and next generation wireless communications: A survey on bidirectional relationshipcited by 2
  3. 2023Towards Zero-trust Security for the Metaversecited by 1
  4. 2023Blockchain and Biometrics: Survey, GDPR Analysis, and Future Directionscited by 1
  5. 2024Personalized Federated Learning Techniques: Empirical Analysiscited by 1
  6. 2022Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucinationcited by 0
  7. 2024Federated Learning Method for Preserving Privacy in Face Recognition Systemcited by 0
  8. 2024Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attackcited by 0
  9. 2024FedEGG: Federated Learning with Explicit Global Guidancecited by 0
  10. 2024IPFed: Identity protected federated learning for user authenticationcited by 0
  11. 2024AdaFedFR: Federated Face Recognition with Adaptive Inter-Class Representation Learningcited by 0
  12. 2024Federated Learning for Face Recognition via Intra-subject Self-supervised Learningcited by 0
  13. 2024Meta-Learning for Federated Face Recognition in Imbalanced Data Regimescited by 0
  14. 2024FedHide: Federated Learning by Hiding in the Neighborscited by 0