English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

As Deep Learning algorithms continue to evolve and become more sophisticated,they require massive datasets for model training and efficacy of models. Someof those data requirements can be met with the help of existing datasets withinthe organizations. Current Machine Learning practices can be leveraged togenerate synthetic data from an existing dataset. Further, it is wellestablished that diversity in generated synthetic data relies on (and isperhaps limited by) statistical properties of available dataset within a singleorganization or entity. The more diverse an existing dataset is, the moreexpressive and generic synthetic data can be. However, given the scarcity ofunderlying data, it is challenging to collate big data in one organization. Thediverse, non-overlapping dataset across distinct organizations provides anopportunity for them to contribute their limited distinct data to a larger poolthat can be leveraged to further synthesize. Unfortunately, this raises dataprivacy 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 syntheticdata among multiple participants in a federated and collaborative network.FedSyn creates a synthetic data generation model, which can generate syntheticdata consisting of statistical distribution of almost all the participants inthe network. FedSyn does not require access to the data of an individualparticipant, hence protecting the privacy of participant's data. The proposedtechnique in this paper leverages federated machine learning and generativeadversarial network (GAN) as neural network architecture for synthetic datageneration. The proposed method can be extended to many machine learningproblem classes in finance, health, governance, technology and many more.

FedSyn: Synthetic Data Generation using Federated Learning