  As the Internet grows in popularity, more and more classification jobs, such
as IoT, finance industry and healthcare field, rely on mobile edge computing to
advance machine learning. In the medical industry, however, good diagnostic
accuracy necessitates the combination of large amounts of labeled data to train
the model, which is difficult and expensive to collect and risks jeopardizing
patients' privacy. In this paper, we offer a novel medical diagnostic framework
that employs a federated learning platform to ensure patient data privacy by
transferring classification algorithms acquired in a labeled domain to a domain
with sparse or missing labeled data. Rather than using a generative adversarial
network, our framework uses a discriminative model to build multiple
classification loss functions with the goal of improving diagnostic accuracy.
It also avoids the difficulty of collecting large amounts of labeled data or
the high cost of generating large amount of sample data. Experiments on
real-world image datasets demonstrates that the suggested adversarial federated
transfer learning method is promising for real-world medical diagnosis
applications that use image classification.
