mlsanityData to 14 Oct 2026

Privacy-Preserving Federated Learning via Dataset Distillation

arXiv 2410.19548 ↗First posted 25 Oct 2024cs.LGcited by 0

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Abstract (arXiv)

Federated Learning (FL) allows users to share knowledge instead of raw data to train a model with high accuracy. Unfortunately, during the training, users lose control over the knowledge shared, which causes serious data privacy issues. We hold that users are only willing and need to share the essential knowledge to the training task to obtain the FL model with high accuracy. However, existing efforts cannot help users minimize the shared knowledge according to the user intention in the FL training procedure. This work proposes FLiP, which aims to bring the principle of least privilege (PoLP) to FL training. The key design of FLiP is applying elaborate information reduction on the training data through a local-global dataset distillation design. We measure the privacy performance through attribute inference and membership inference attacks. Extensive experiments show that FLiP strikes a good balance between model accuracy and privacy protection.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 25 Oct 2024.

Builds on

11 of 21 references.

References · 11

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2016Deep Learning with Differential Privacycited by 2,728
  4. 2021Dataset Condensation with Distribution Matchingcited by 365
  5. 2021Dataset Condensation with Differentiable Siamese Augmentationcited by 291
  6. 2023Dataset Distillation: A Comprehensive Reviewcited by 127
  7. 2019Soft-Label Dataset Distillation and Text Dataset Distillationcited by 107
  8. 2020Wireless Federated Learning with Local Differential Privacycited by 52
  9. 2020Privacy-preserving Collaborative Learning with Automatic Transformation Searchcited by 31
  10. 2021A Novel Attribute Reconstruction Attack in Federated Learningcited by 18
  11. 2022Concealing Sensitive Samples against Gradient Leakage in Federated Learningcited by 11

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  1. 2022Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation
  2. 2025Federated One-Shot Learning with Data Privacy and Objective-Hiding
  3. 2024Privacy in Federated Learning
  4. 2025Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation
  5. 2023Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher
  6. 2025Secure Federated Data Distillation
  7. 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
  8. 2024Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator