MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-IID distribution
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Abstract (arXiv)
Federated learning is an emerging concept in the domain of distributed machine learning. This concept has enabled GANs to benefit from the rich distributed training data while preserving privacy. However, in a non-iid setting, current federated GAN architectures are unstable, struggling to learn the distinct features and vulnerable to mode collapse. In this paper, we propose a novel architecture MULTI-FLGAN to solve the problem of low-quality images, mode collapse and instability for non-iid datasets. Our results show that MULTI-FLGAN is four times as stable and performant (i.e. high inception score) on average over 20 clients compared to baseline FLGAN.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 16 of its 35 references that are in the map. First posted on arXiv 24 Jun 2022.
Builds on
16 of 35 references.
References · 16
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4 more
- 2016Generative Multi-Adversarial Networkscited by 60
- 2018MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasetscited by 49
- 2018Convergence Problems with Generative Adversarial Networks (GANs)cited by 14
- 2022PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networkscited by 14
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.