Abstract

There are two major challenges in artificial intelligence field today. One is that in most industries, data exists in the form of isolated islands. The other is the increasing demand for AI to be aware of user privacy and data security. We give an overview of these challenges and survey recent works on secure federated learning to meet them. We will describe the federated learning framework by considering horizontal federated learning, vertical federated learning and federated transfer learning. We provide definitions, architectures and applications for the federated learning framework. We also survey related works in AI security, privacy and confidentiality. We will show some typical application scenarios of the technology. Finally we describe a development roadmap for federated and transfer learning.

Speakers

  • Yang Liu

    WeBank

    Yang Liu is a Senior Researcher in AI Department of WeBank, China. Her research interests include machine learning, federated learning, federated transfer learning, and applications of these technologies in the financial industry. She received her PhD in Chemical and Biological Engineering from Princeton University in 2012 and her Bachelor's degree from Tsinghua University.

  • Qiang Yang

    Hong Kong University of Science and Technology

    Qiang Yang is a Chair Professor in Department of Computer Science and Engineering, the Hong Kong University of Science and Technology. He received his PhD from Computer Science Department of the University of Maryland, College Park in 1989. He is a Fellow of AAAI, ACM, IEEE, AAAS, and IAPR. His professional services include being the IJCAI President (2017-2019), ACM SIGART (SIGAI) Vice Chair (2009-2012); PC Chair of IJCAI (2015), ACM KDD (2010); Conference Chair of ACM KDD (2012). He is the Editor in Chief of IEEE Transactions on Big Data.

  • Tianjian Chen

    WeBank

    Tianjian Chen is the Deputy General Manager of AI Department of WeBank, China. He received his Bachelor's degree in Electronic Engineering from Tsinghua University in 2006. He had been a Software Engineer (2006-2008) and Senior Software Engineer (2008-2009) at Baidu, System Architect at Xunlei Networking Technologies (2009), System Architect at BGI (2010-2011), Senior Architect (2011-2014) and Principal Architect (2014-2018) at Baidu.

  • Zhuoshi Wei

    WeBank

    Zhuoshi Wei is a Researcher in AI Department of WeBank, China. She received her B.S. from School of Electronic and Information Engineering, Beijing Jiaotong University in 2003, and her PhD in Pattern Recognition and Intelligent System from the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences in 2009. She had been a postdoctoral researcher at the Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, USA (2010-2013). She was a Principal Data Scientist at Capital One Financial Corporation (2015-2017).

Schedule

Sunday, January 27 (1:30PM - 3:15PM), 2019
  • Who We Are
  • Challenges for AI Industry: Privacy, Security and Confidentiality
  • Federated Learning
  • Privacy-preserving Machine Learning
  • Federated Transfer Learning
  • Building Federated AI Applications