论文标题
多人：用于非IID分布的多分布的对抗网络
MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-IID distribution
论文作者
论文摘要
联合学习是分布式机器学习领域中的一个新兴概念。这个概念使甘斯能够从保留隐私的同时从丰富的分布式培训数据中受益。但是，在非IID设置中，当前联合的GAN体系结构是不稳定的，努力学习独特的功能并容易崩溃。在本文中，我们提出了一种新型的体系结构多流体，以解决非IID数据集的低质量图像，模式崩溃和不稳定性的问题。我们的结果表明，与基线Flgan相比，多流gan的平均稳定性和表现量（即高启动评分）平均是20多倍。
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.
