mlsanityData to 14 Oct 2026

Federated Generative Privacy

arXiv 1910.08385 ↗First posted 18 Oct 2019stat.ML cs.CR cs.DCcited by 16

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Abstract (arXiv)

In this paper, we propose FedGP, a framework for privacy-preserving data release in the federated learning setting. We use generative adversarial networks, generator components of which are trained by FedAvg algorithm, to draw privacy-preserving artificial data samples and empirically assess the risk of information disclosure. Our experiments show that FedGP is able to generate labelled data of high quality to successfully train and validate supervised models. Finally, we demonstrate that our approach significantly reduces vulnerability of such models to model inversion attacks.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Oct 2019.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 4; 2024: 3.

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  1. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 120
  2. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 66
  3. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
  4. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  5. 2021Generative Adversarial Networks: A Survey Towards Private and Secure Applicationscited by 15
  6. 2021Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversarycited by 14
  7. 2022DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decodercited by 14
  8. 2022Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Datacited by 12
  9. 2022A review of Federated Learning in Intrusion Detection Systems for IoTcited by 4
  10. 2022ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scalecited by 4
  11. 2023Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Trainingcited by 4
  12. 2023A Unified View of Differentially Private Deep Generative Modelingcited by 4
4 more
  1. 2023SoK: Memorisation in machine learningcited by 1
  2. 2024Harnessing Federated Generative Learning for Green and Sustainable Internet of Thingscited by 1
  3. 2024Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentationcited by 1
  4. 2024Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesiscited by 0