FeTGAN: Federated Time-Series Generative Adversarial Network

Abstract

The key to producing high-fidelity time-series data is to preserve temporal dynamics. This means that generated sequences respect the relationship between variables across time as in the original data. While new types of GANs have been used to generate time-series data, they, like previous GAN
implementations, are time consuming to train. A novel federated framework is proposed, which generates realistic time-series data, by combining supervised and unsupervised training. The framework is based on the work in TimeGAN and Federated GAN (FeGAN). Using an embedded learning space, TimeGAN
encourages the network to mimic the structure of the training data. FeGAN allows the results of TimeGAN to be combined at a central server, which has benefits for both throughput, and potential to improve data privacy. This also introduces the possibility of using cross domain data. The challenge with creating applying federated learning to TimeGAN, and timeseries data in general is whether the learned temporal dynamics can be combined. This is accomplished by the combination of the weighting and sampling scheme used. This paper demonstrates, by qualitative and quantitative analysis, the ability novel framework proposed, to produce equivalent quality synthetic timeseries data compared to the original TimeGAN, without sharing local data between nodes in the network. This is based on the predictive and discriminative scores described, as well as PCA and t-SNE analysis. Additionally, there is an approximate eleven percent increase in Floating Point Operations per second when using one machine, and up to a thirty percent increase when using multiple.