  As Deep Learning algorithms continue to evolve and become more sophisticated,
they require massive datasets for model training and efficacy of models. Some
of those data requirements can be met with the help of existing datasets within
the organizations. Current Machine Learning practices can be leveraged to
generate synthetic data from an existing dataset. Further, it is well
established that diversity in generated synthetic data relies on (and is
perhaps limited by) statistical properties of available dataset within a single
organization or entity. The more diverse an existing dataset is, the more
expressive and generic synthetic data can be. However, given the scarcity of
underlying data, it is challenging to collate big data in one organization. The
diverse, non-overlapping dataset across distinct organizations provides an
opportunity for them to contribute their limited distinct data to a larger pool
that can be leveraged to further synthesize. Unfortunately, this raises data
privacy concerns that some institutions may not be comfortable with.
  This paper proposes a novel approach to generate synthetic data - FedSyn.
FedSyn is a collaborative, privacy preserving approach to generate synthetic
data among multiple participants in a federated and collaborative network.
FedSyn creates a synthetic data generation model, which can generate synthetic
data consisting of statistical distribution of almost all the participants in
the network. FedSyn does not require access to the data of an individual
participant, hence protecting the privacy of participant's data. The proposed
technique in this paper leverages federated machine learning and generative
adversarial network (GAN) as neural network architecture for synthetic data
generation. The proposed method can be extended to many machine learning
problem classes in finance, health, governance, technology and many more.
