  Protecting data privacy is paramount in the fields such as finance, banking,
and healthcare. Federated Learning (FL) has attracted widespread attention due
to its decentralized, distributed training and the ability to protect the
privacy while obtaining a global shared model. However, FL presents challenges
such as communication overhead, and limited resource capability. This motivated
us to propose a two-stage federated learning approach toward the objective of
privacy protection, which is a first-of-its-kind study as follows: (i) During
the first stage, the synthetic dataset is generated by employing two different
distributions as noise to the vanilla conditional tabular generative
adversarial neural network (CTGAN) resulting in modified CTGAN, and (ii) In the
second stage, the Federated Probabilistic Neural Network (FedPNN) is developed
and employed for building globally shared classification model. We also
employed synthetic dataset metrics to check the quality of the generated
synthetic dataset. Further, we proposed a meta-clustering algorithm whereby the
cluster centers obtained from the clients are clustered at the server for
training the global model. Despite PNN being a one-pass learning classifier,
its complexity depends on the training data size. Therefore, we employed a
modified evolving clustering method (ECM), another one-pass algorithm to
cluster the training data thereby increasing the speed further. Moreover, we
conducted sensitivity analysis by varying Dthr, a hyperparameter of ECM at the
server and client, one at a time. The effectiveness of our approach is
validated on four finance and medical datasets.
