Federated Machine Learning is a new paradigm considering both joint use of isolated data and the privacy and security of data, which was first proposed by Google in 2016 and then was extended into a comprehensive secure learning framework. It can be briefly categorized as horizontal federated learning, vertical federated learning, and federated transfer learning. This tutorial provides an introduction to federated learning including the basic concepts, architectures, and some applications, with the fundamental idea that federated mechanisms are approaches to allowing knowledge to be shared without compromising user privacy.

Xizhao WANG served in Hebei University as a professor and the dean of school of Mathematics and Computer Sciences before 2014. After 2014 Prof. Wang worked as a professor in Big Data Institute of Shenzhen University. Prof. Wang’s major research interests include uncertainty modeling and machine learning. He has edited 10+ special issues and published 3 monographs, 2 textbooks, and 200+ peer-reviewed research papers. As a Principle Investigator (PI) or co-PI, Prof. Wang's has completed 30+ research projects, and supervised more than 200 Mphil and PhD students.

Prof. Wang is a CAAI Fellow, an IEEE Fellow, the previous BoG member of IEEE SMC society, the chair of IEEE SMC Technical Committee on Computational Intelligence, the Chief Editor of Machine Learning and Cybernetics Journal, and associate editors for a couple of journals such as IEEE Transactions on Fuzzy Systems, on Cybernetics, etc. He was the recipient of the IEEE SMCS Outstanding Contribution Award in 2004 and the recipient of IEEE SMCS Best Associate Editor Award in 2006. He is the general Co-Chair of the 2002–2021 International Conferences on Machine Learning and Cybernetics. Prof. Wang was a distinguished lecturer of the IEEE SMCS.