# Framework Construction of an Adversarial Federated Transfer Learning Classifier

arXiv: https://arxiv.org/abs/2211.04734 · HTML: https://mlsanity.com/paper/2211.04734/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 55 references that are in the map.
- First posted on arXiv 9 Nov 2022.

## Builds on (most cited first, 15 of 15)

- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Federated Adversarial Domain Adaptation](https://mlsanity.com/paper/1911.02054/) (2019)
- [Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation](https://mlsanity.com/paper/1705.08302/) (2017)
- [Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification](https://mlsanity.com/paper/1608.04363/) (2016)
- [End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network](https://mlsanity.com/paper/1704.06065/) (2017)
- [Neural Image Compression for Gigapixel Histopathology Image Analysis](https://mlsanity.com/paper/1811.02840/) (2018)
- [Secure and Efficient Federated Transfer Learning](https://mlsanity.com/paper/1910.13271/) (2019)
- [Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention](https://mlsanity.com/paper/2006.10517/) (2020)
- [Remove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer](https://mlsanity.com/paper/2002.05844/) (2020)
- [4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection](https://mlsanity.com/paper/2007.11450/) (2020)
- [Reconstructing undersampled photoacoustic microscopy images using deep learning](https://mlsanity.com/paper/2006.00251/) (2020)
- [Efficient ADMM-based Algorithms for Convolutional Sparse Coding](https://mlsanity.com/paper/2109.02969/) (2021)
- [Multi-modal Visual Place Recognition in Dynamics-Invariant Perception Space](https://mlsanity.com/paper/2105.07800/) (2021)

## Abstract (arXiv)

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.