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Related Experiment Video

Updated: Jan 13, 2026

Deep Neural Networks for Image-Based Dietary Assessment
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Federated Self-Supervised Few-Shot Face Recognition.

Nursultan Makhanov1, Beibut Amirgaliyev1, Talgat Islamgozhayev1

  • 1Smart City Research Center, Astana IT University, Astana 010000, Kazakhstan.

Journal of Imaging
|October 28, 2025
PubMed
Summary

Federated learning for face recognition preserves privacy but reduces accuracy by 12-30%. Traditional CNNs perform better than transformers in this federated few-shot learning setting.

Keywords:
face recognitionfederated learningfew-shot learningprivacy-preserving machine learningself-supervised learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Privacy-preserving machine learning is crucial for sensitive data like facial information.
  • Federated learning (FL), self-supervised learning (SSL), and few-shot learning (FSL) are advanced techniques with potential for privacy-preserving face recognition.

Purpose of the Study:

  • To develop and evaluate a systematic framework combining FL, SSL, and FSL for privacy-preserving face recognition.
  • To quantify the performance trade-offs associated with privacy preservation in face recognition systems.

Main Methods:

  • Self-supervised pre-training using SimCLR on CASIA-WebFace dataset in a federated setting.
  • Federated few-shot fine-tuning on LFW dataset utilizing prototypical networks.
  • Comprehensive evaluation across six architectures: ResNet, DenseNet, MobileViT, ViT-Small, CvT, and CoAtNet.

Main Results:

  • The federated framework successfully preserves data privacy but incurs significant performance degradation (12-30% accuracy loss) compared to centralized methods.
  • Convolutional Neural Networks (CNNs) demonstrated greater robustness under federated constraints than transformer-based architectures.
  • Five-shot learning configurations offered an optimal balance between data efficiency and recognition performance.

Conclusions:

  • Privacy preservation in federated face recognition comes at a substantial performance cost.
  • CNNs are more suitable than transformers for federated few-shot face recognition tasks.
  • Empirical benchmarks and insights are provided for deploying privacy-preserving face recognition systems, highlighting critical privacy-utility trade-offs.