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.
翻译:随着互联网越来越受欢迎,越来越多的分类工作,如IoT、金融业和保健领域,都依赖移动边缘计算来推进机器学习;然而,在医疗行业,良好的诊断准确性要求将大量标签数据结合起来来培训模型,而模型的收集十分困难,费用昂贵,并有危害病人隐私的风险。在本文中,我们提供了一个新的医学诊断框架,利用一个联合学习平台来确保病人数据隐私,通过将标签领域获得的分类算法转移到有稀有或缺失的标签数据的领域。我们的框架不是使用基因化对抗网络,而是使用一种歧视性模型来建立多重分类损失功能,目的是提高诊断准确性。它还避免了收集大量标签数据的困难,或产生大量抽样数据的高昂成本。关于真实世界图像数据集的实验表明,建议的对抗式联邦化转移学习方法对于使用图像分类的现实世界医学诊断应用很有希望。