In this work, we review the architecture design of existing federated General Adversarial Networks (GAN) solutions and highlight the security and trust-related weaknesses in the existing designs. We then describe how these weaknesses make existing designs unsuitable for the requirements needed for a consortium of health registries working towards generating synthetic datasets for research purposes. Moreover, we propose how these weaknesses can be addressed with our novel architecture solution. Our novel architecture solution combines several building blocks to generate synthetic data in a decentralised setting. Consortium blockchains, secure multi-party computations, and homomorphic encryption are the core building blocks of our proposed architecture solution to address the weaknesses in the existing design of federated GANs. Finally, we discuss our proposed solution's advantages and future research directions.

翻译：在这项工作中,我们审查了现有的联合通用反versarial网络(GAN)的建筑设计,并突出了现有设计中与安全和信任有关的弱点。然后我们描述了这些弱点如何使现有设计不适合卫生登记处联合会为研究目的编制合成数据集所需要的要求。此外,我们建议如何用我们的新建筑解决方案来克服这些弱点。我们的新建筑解决方案将几个组成部分结合起来,以便在分散的环境下生成合成数据。联盟的连锁、安全的多党计算和同质加密是我们提议的建筑解决方案的核心组成部分,目的是解决联邦式全球网络现有设计中的弱点。最后,我们讨论了我们提出的解决方案的优势和未来研究方向。