Framework Construction of an Adversarial Federated Transfer Learning Classifier

TL;DR

This paper introduces a novel federated transfer learning framework for medical image diagnosis that preserves patient privacy and improves accuracy without requiring extensive labeled data or generative models.

Contribution

It proposes a discriminative adversarial federated transfer learning framework that enhances diagnostic accuracy while protecting patient data privacy in medical imaging.

Findings

Effective in real-world medical image classification tasks

Improves diagnostic accuracy with limited labeled data

Avoids the complexity of generative adversarial networks

Abstract

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.