Keynotes

Abstract: Quite often in real-world environment, data can be available only locally coming with strict constraints imposed on their usage beyond individual data islands. Such restrictions constitute genuine conceptual and algorithmic challenges when it comes to solving problems of global data analysis and the development of global models. A lot of pursuits located in this realm fall under the umbrella of federated learning where this learning paradigm is linked with the computing ideas of cloud computing.

The discovery of the structure of data to be realized at the global level becomes highly demanding given the required privacy of attributes to address the existing challenge, a number of ways of conceptualization of the collaborative clustering are discussed. In spite of the diversity of the ensuing optimization mechanism, it is worth stressing that building a global structure is completed at a higher level of abstraction. Subsequently, it is shown that a sound conceptual setting involves concepts of information granules and mechanisms of Granular Computing. The quality of constructed relationships is also analyzed at the level of granular constructs. The accommodation of privacy constraints in the design of rule-based architectures with the rules assuming the form “if condition is Ai then y=fi(x), i=1,2,...,c is elaborated in detail. The attributes not publicly available are expressed through information granules Ai built through collaborative clustering whereas remaining attributes (x) are used in the construction of the local functions. A way of forming the rules through unsupervised federated learning is investigated along with the detailed algorithmic developments. A granular characterization of the obtained model formed by the server vis-a-vis data located at the level of individual clients is discussed and a number of case studies is studied.

Bio: Witold Pedrycz (IEEE Life Fellow) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He is a recipient of several awards including Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society. His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery, pattern recognition, data science, knowledge-based neural networks among others. Dr. Pedrycz is involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Co-editor-in-Chief of Int. J. of Granular Computing (Springer) and J. of Data Information and Management (Springer).