Framework Construction of an Adversarial Federated Transfer Learning Classifier
This addresses privacy and data scarcity issues in medical diagnosis, but it is incremental as it builds on existing federated and transfer learning methods.
The paper tackles the problem of training accurate medical diagnostic models without compromising patient privacy or requiring large labeled datasets by proposing an adversarial federated transfer learning framework. The result shows promising performance on real-world image datasets for medical diagnosis applications.
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.