FedGAN: Federated Generative Adversarial Networks for Distributed Data
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Abstract (arXiv)
We propose Federated Generative Adversarial Network (FedGAN) for training a GAN across distributed sources of non-independent-and-identically-distributed data sources subject to communication and privacy constraints. Our algorithm uses local generators and discriminators which are periodically synced via an intermediary that averages and broadcasts the generator and discriminator parameters. We theoretically prove the convergence of FedGAN with both equal and two time-scale updates of generator and discriminator, under standard assumptions, using stochastic approximations and communication efficient stochastic gradient descents. We experiment FedGAN on toy examples (2D system, mixed Gaussian, and Swiss role), image datasets (MNIST, CIFAR-10, and CelebA), and time series datasets (household electricity consumption and electric vehicle charging sessions). We show FedGAN converges and has similar performance to general distributed GAN, while reduces communication complexity. We also show its robustness to reduced communications.
Key facts. Cited by 89 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2020.
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Citing papers per year: 2021: 18; 2022: 21; 2023: 13; 2024: 16; 2025: 13; 2026: 8.
89 papers.
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- 2023Better Generative Replay for Continual Federated Learningcited by 53
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- 2023FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning Systemcited by 38
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- 2022One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesiscited by 22
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- 2026Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation21 Jun 2026
- 2026FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inference9 May 2026
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- 2026The Gaussian-Head OFL Family: One-Shot Federated Learning from Client Global Statistics1 Feb 2026
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- 2025Calibrating Biased Distribution in VFM-derived Latent Space via Cross-Domain Geometric Consistency19 Aug 2025
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