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In this work, we review the architecture design of existing federated GeneralAdversarial Networks (GAN) solutions and highlight the security andtrust-related weaknesses in the existing designs. We then describe how theseweaknesses make existing designs unsuitable for the requirements needed for aconsortium of health registries working towards generating synthetic datasetsfor research purposes. Moreover, we propose how these weaknesses can beaddressed with our novel architecture solution. Our novel architecture solutioncombines several building blocks to generate synthetic data in a decentralisedsetting. Consortium blockchains, secure multi-party computations, andhomomorphic encryption are the core building blocks of our proposedarchitecture solution to address the weaknesses in the existing design offederated GANs. Finally, we discuss our proposed solution's advantages andfuture research directions.

Generating Synthetic Data in a Secure Federated General Adversarial Networks for a Consortium of Health Registries