Recent advances of generative learning models are accompanied by the growing interest in federated learning (FL) based on generative adversarial network (GAN) models. In the context of FL, GAN can capture the underlying client data structure, and regenerate samples resembling the original data distribution without compromising the private raw data. Although most existing GAN-based FL works focus on training a global model, Personalized FL (PFL) sometimes can be more effective in view of client data heterogeneity in terms of distinct data sample distributions, feature spaces, and labels. To cope with client heterogeneity in GAN-based FL, we propose a novel GAN sharing and aggregation strategy for PFL. The proposed PFL-GAN addresses the client heterogeneity in different scenarios. More specially, we first learn the similarity among clients and then develop an weighted collaborative data aggregation. The empirical results through the rigorous experimentation on several well-known datasets demonstrate the effectiveness of PFL-GAN.

翻译：近年来，生成学习模型的进步伴随着基于生成对抗网络（GAN）模型的联邦学习（FL）日益增长的兴趣。在FL背景下，GAN能够捕获底层客户端数据结构，并重新生成与原始数据分布相似的样本，同时不损害私有原始数据。尽管现有大多数基于GAN的FL工作侧重于训练全局模型，但考虑到客户端在数据样本分布、特征空间和标签方面的异质性，个性化FL（PFL）有时可能更为有效。为了应对基于GAN的FL中的客户端异构性，我们提出了一种用于PFL的新型GAN共享与聚合策略。所提出的PFL-GAN在不同场景下解决了客户端异构性。更具体地，我们首先学习客户端之间的相似性，然后开发了一种加权协同数据聚合方法。通过在几个知名数据集上的严格实验，实证结果证明了PFL-GAN的有效性。