# PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy

arXiv: https://arxiv.org/abs/2305.11437 · HTML: https://mlsanity.com/paper/2305.11437/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 40 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 22 of 22)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data](https://mlsanity.com/paper/1811.11479/) (2018)
- [No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data](https://mlsanity.com/paper/2106.05001/) (2021)
- [Privacy and Robustness in Federated Learning: Attacks and Defenses](https://mlsanity.com/paper/2012.06337/) (2020)
- [Consistency Regularization for Generative Adversarial Networks](https://mlsanity.com/paper/1910.12027/) (2019)
- [FedMix: Approximation of Mixup under Mean Augmented Federated Learning](https://mlsanity.com/paper/2107.00233/) (2021)
- [Towards Fair Federated Learning with Zero-Shot Data Augmentation](https://mlsanity.com/paper/2104.13417/) (2021)
- [FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning](https://mlsanity.com/paper/2111.08211/) (2021)
- [LOGAN: Membership Inference Attacks Against Generative Models](https://mlsanity.com/paper/1705.07663/) (2017)
- [Federated Learning with GAN-based Data Synthesis for Non-IID Clients](https://mlsanity.com/paper/2206.05507/) (2022)
- [Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks](https://mlsanity.com/paper/2209.15595/) (2022)
- [PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks](https://mlsanity.com/paper/2202.09155/) (2022)
- [Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors](https://mlsanity.com/paper/2105.03812/) (2021)

## Built on by (most cited first, 4 of 4)

- [GPT-FL: Generative Pre-trained Model-Assisted Federated Learning](https://mlsanity.com/paper/2306.02210/) (2023)
- [A Systematic Review of Federated Generative Models](https://mlsanity.com/paper/2405.16682/) (2024)
- [PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning](https://mlsanity.com/paper/2308.12454/) (2023)
- [UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Data](https://mlsanity.com/paper/2308.05870/) (2023)

## Abstract (arXiv)

Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation owing to its strong potential in capturing underlying data statistics while preserving data privacy. However, in cases of practical data heterogeneity among FL clients, existing FL frameworks still exhibit deficiency in capturing the overall feature properties of local client data that exhibit disparate distributions. In response, generative adversarial networks (GANs) have recently been exploited in FL to address data heterogeneity since GANs can be integrated for data regeneration without exposing original raw data. Despite some successes, existing GAN-related FL frameworks often incur heavy communication cost and also elicit other privacy concerns, which limit their applications in real scenarios. To this end, this work proposes a novel FL framework that requires only partial GAN model sharing. Named as PS-FedGAN, this new framework enhances the GAN releasing and training mechanism to address heterogeneous data distributions across clients and to strengthen privacy preservation at reduced communication cost, especially over wireless networks. Our analysis demonstrates the convergence and privacy benefits of the proposed PS-FEdGAN framework. Through experimental results based on several well-known benchmark datasets, our proposed PS-FedGAN shows great promise to tackle FL under non-IID client data distributions, while securing data privacy and lowering communication overhead.