Privacy issues were raised in the process of training deep learning in medical, mobility, and other fields. To solve this problem, we present privacy-preserving distributed deep learning method that allow clients to learn a variety of data without direct exposure. We divided a single deep learning architecture into a common extractor, a cloud model and a local classifier for the distributed learning. First, the common extractor, which is used by local clients, extracts secure features from the input data. The secure features also take the role that the cloud model can employ various task and diverse types of data. The feature contain the most important information that helps to proceed various task. Second, the cloud model including most parts of the whole training model gets the embedded features from the massive local clients, and performs most of deep learning operations which takes severe computing cost. After the operations in cloud model finished, outputs of the cloud model send back to local clients. Finally, the local classifier determined classification results and delivers the results to local clients. When clients train models, our model does not directly expose sensitive information to exterior network. During the test, the average performance improvement was 2.63% over the existing local training model. However, in a distributed environment, there is a possibility of inversion attack due to exposed features. For this reason, we experimented with the common extractor to prevent data restoration. The quality of restoration of the original image was tested by adjusting the depth of the common extractor. As a result, we found that the deeper the common extractor, the restoration score decreased to 89.74.
To Split or Not to Split: The Impact of Disparate Treatment in Classification
Harvard University（哈佛大学）；Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS), Universidad Nacional Autónoma de México（墨西哥国立自治大学应用数学与系统研究所（IIMAS））
Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classification
Department of Electronic Engineering, Wonkwang University（Wonkwang大学电子工程系）；RSC LAB, MODULABS（MODULABS的RSC实验室）；AICU Global Inc.（AICU Global公司）；Seoul National University Bundang Hospital（首尔国立大学柏阳医院）
Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification
Centre for Wireless Communications (CWC), University of Oulu（奥卢大学无线通信中心）；CEMSE Division, King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学CEMSE学部）
Functional Random Forest with Adaptive Cost-Sensitive Splitting for Imbalanced Functional Data Classification
Fahad Mostafa School of Mathematical and Natural Sciences, Arizona State University, USA（法哈德·莫斯塔法 数学与自然科学学院，亚利桑那州立大学）；Hafiz Khan Department of Public Health, Julia Jones Matthews School of Population and Public Health, Texas Tech Health Sciences Center, USA（哈菲兹·汗 公共卫生部，朱莉亚·琼斯·马修斯流行病学与公共卫生学院，德克萨斯技术健康科学中心）
LHT: Statistically-Driven Oblique Decision Trees for Interpretable Classification
Department of Automation（自动化系）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Department of Computing Science（计算机科学系）；University of Alberta（阿尔伯塔大学）
A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset
University of British Columbia（不列颠哥伦比亚大学）；BC Cancer Research Institute（不列颠哥伦比亚癌症研究所）；Microsoft（微软公司）；Seoul St. Mary’s Hospital（首尔圣玛丽医院）；The Catholic University of Korea（韩国天主教大学）；BC Cancer（不列颠哥伦比亚癌症中心）
