mlsanityData to 14 Oct 2026

Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing

arXiv 2407.20830 ↗First posted 30 Jul 2024cs.LG cs.AI cs.CVcited by 7

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Abstract (arXiv)

Federated learning has emerged as a paradigm for collaborative learning, enabling the development of robust models without the need to centralise sensitive data. However, conventional federated learning techniques have privacy and security vulnerabilities due to the exposure of models, parameters or updates, which can be exploited as an attack surface. This paper presents Federated Knowledge Recycling (FedKR), a cross-silo federated learning approach that uses locally generated synthetic data to facilitate collaboration between institutions. FedKR combines advanced data generation techniques with a dynamic aggregation process to provide greater security against privacy attacks than existing methods, significantly reducing the attack surface. Experimental results on generic and medical datasets show that FedKR achieves competitive performance, with an average improvement in accuracy of 4.24% compared to training models from local data, demonstrating particular effectiveness in data scarcity scenarios.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 27 of its 37 references that are in the map. First posted on arXiv 30 Jul 2024.

Builds on

27 of 37 references.

References · 27

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  6. 2019Deep Leakage from Gradientscited by 1,021
  7. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  8. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  9. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 699
  10. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 690
  11. 2017Evasion Attacks against Machine Learning at Test Timecited by 689
  12. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
15 more
  1. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 309
  2. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  3. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 156
  4. 2018ATOMO: Communication-efficient Learning via Atomic Sparsificationcited by 136
  5. 2019Classification Accuracy Score for Conditional Generative Modelscited by 136
  6. 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
  7. 2020FedGAN: Federated Generative Adversarial Networks for Distributed Datacited by 90
  8. 2019Generative Models for Effective ML on Private, Decentralized Datasetscited by 85
  9. 2020Free-rider Attacks on Model Aggregation in Federated Learningcited by 57
  10. 2021SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learningcited by 15
  11. 2023Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniquescited by 11
  12. 2022On the utility and protection of optimization with differential privacy and classic regularization techniquescited by 10
  13. 2024Stable Diffusion Dataset Generation for Downstream Classification Taskscited by 9
  14. 2024Synthetic Image Learning: Preserving Performance and Preventing Membership Inference Attackscited by 5
  15. 2023Discriminative Adversarial Privacy: Balancing Accuracy and Membership Privacy in Neural Networkscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 5.

7 papers.

  1. 2025Your Image Generator Is Your New Private Datasetcited by 2
  2. 2026PolyGen: Fully Synthetic Vision-Language Training via Multi-Generator Ensemblescited by 1
  3. 2026Inference-Time Refinement Closes the Synthetic-Real Gap in Tabular Diffusioncited by 1
  4. 2026Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomycited by 1
  5. 2025Neuro-Symbolic Scene Graph Conditioning for Synthetic Image Dataset Generationcited by 0
  6. 2026Concordia: Self-Improving Synthetic Tables for Federated LLMscited by 0
  7. 2026Beyond Weights and Gradients: A Taxonomy of Federated Learning Messagescited by 0