# Federated Generative Privacy

arXiv: https://arxiv.org/abs/1910.08385 · HTML: https://mlsanity.com/paper/1910.08385/

## Key facts

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

## Built on by (most cited first, 16 of 16)

- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021)
- [FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks](https://mlsanity.com/paper/2111.11066/) (2021)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Generative Adversarial Networks: A Survey Towards Private and Secure Applications](https://mlsanity.com/paper/2106.03785/) (2021)
- [Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary](https://mlsanity.com/paper/2101.07235/) (2021)
- [DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decoder](https://mlsanity.com/paper/2211.11591/) (2022)
- [Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data](https://mlsanity.com/paper/2206.00686/) (2022)
- [A review of Federated Learning in Intrusion Detection Systems for IoT](https://mlsanity.com/paper/2204.12443/) (2022)
- [ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale](https://mlsanity.com/paper/2207.09078/) (2022)
- [Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training](https://mlsanity.com/paper/2308.16376/) (2023)
- [A Unified View of Differentially Private Deep Generative Modeling](https://mlsanity.com/paper/2309.15696/) (2023)
- [SoK: Memorisation in machine learning](https://mlsanity.com/paper/2311.03075/) (2023)
- [Harnessing Federated Generative Learning for Green and Sustainable Internet of Things](https://mlsanity.com/paper/2407.05915/) (2024)
- [Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentation](https://mlsanity.com/paper/2412.08460/) (2024)
- [Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis](https://mlsanity.com/paper/2411.01929/) (2024)

## 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.