ICOIN 2021 Online Conference
Tutorials
TUTORIAL #1
  • "Measurement and modeling of mm-wave and THz channels"
  • Andreas Molisch
  • University of Southern California, USA
Abstract

With the imminent large-scale deployment of 5G and the emergence of 6G research, there is an increased interest in understanding the propagation channels for the bands from 10-1000 GHz, which will play a vital role in those systems. This talk will first review some propagation basics, and then present a number of recent results in the measurement and deterministic simulation of such channels, with an emphasis will be on double-directional and/or dynamic channel characteristics. We will discuss, among other aspects, how different streets have different pathloss coefficients, how passing cars alternate between being reflectors and blockers, how trees and other objects near windows critically impact outdoor-to-indoor propagation, and that some first THz measurements indicate surprisingly large angular dispersion. A discussion on how to model all these effects, and how they impact system performance, will round off the tutorial.

Biography

Andreas F. Molisch is the Solomon-Golomb - Andrew and Erna Viterbi Chair Professor at the University of Southern California. His current research interests include wireless propagation channels, as well as MIMO, localization, new modulation methods, and joint communication, computation, and caching. He is a Fellow of the National Academy of Inventors, IEEE, AAAS, IET, Member of the Austrian Academy of Sciences, and recipient of numerous awards.

TUTORIAL #2
  • "Communication-efficient and Distributed Machine Learning over Wireless Communications"
  • Mehdi Bennis
  • University of Oulu, Finland
Abstract

Breakthroughs in machine learning (ML) and particularly deep learning have transformed every aspects of our lives from face recognition, medical diagnosis, and natural language processing. This progress has been fueled mainly by the availability of more data and more computing power. However, the current premise in classical ML is based on a single node in a centralized and remote data center with full access to a global dataset and a massive amount of storage and computing. Nevertheless, the advent of a new breed of intelligent devices ranging from drones to self-driving vehicles, makes cloud-based ML inadequate. This talk will present the vision of distributed edge intelligence featuring key enablers, architectures, algorithms and some recent results.

Biography

Dr. Mehdi Bennis is an Associate Professor at the Centre for Wireless Communications, University of Oulu, Finland, an Academy of Finland Research Fellow and head of the intelligent connectivity and networks/systems group (ICON). His main research interests are in radio resource management, heterogeneous networks, game theory and machine learning in 5G networks and beyond. He has co-authored one book and published more than 200 research papers in international conferences, journals and book chapters. He has been the recipient of several prestigious awards including the 2015 Fred W. Ellersick Prize from the IEEE Communications Society, the 2016 Best Tutorial Prize from the IEEE Communications Society, the 2017 EURASIP Best paper Award for the Journal of Wireless Communications and Networks, the all-University of Oulu award for research, In 2019 Dr Bennis received the IEEE ComSoc Radio Communications Committee Early Achievement Award.

TUTORIAL #3
  • "Reliable Federated Learning for Mobile Networks"
  • Dusit (Tao) Niyato
  • Nanyang Technological University, Singapore
Abstract

Federated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, e.g., mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In the federated learning, training data is widely distributed and maintained on the mobile devices as workers. A central aggregator updates a global model by collecting local updates from mobile devices using their local training data to train the global model in each iteration. However, unreliable data may be uploaded by the mobile devices (i.e., workers), leading to frauds in tasks of federated learning. The workers may perform unreliable updates intentionally, e.g., the data poisoning attack, or unintentionally, e.g., low-quality data caused by energy constraints or high-speed mobility. Therefore, finding out trusted and reliable workers in federated learning tasks becomes critical. In this talk, the concept of reputation is introduced as a metric. Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks. Consortium blockchain is leveraged as a decentralized approach for achieving efficient reputation management of the workers without repudiation and tampering. The proposed approach is demonstrated to improve the reliability of federated learning tasks in mobile networks.

Biography

Dusit Niyato is currently a professor in the School of Computer Science and Engineering and, by courtesy, School of Physical & Mathematical Sciences, at the Nanyang Technological University, Singapore. He received B.E. from King Mongkuk’s Institute of Technology Ladkrabang (KMITL), Thailand in 1999 and Ph.D. in Electrical and Computer Engineering from the University of Manitoba, Canada in 2008. He has published more than 380 technical papers in the area of wireless and mobile networking, and is an inventor of four US and German patents. He has authored four books including "Game Theory in Wireless and Communication Networks: Theory, Models, and Applications" with Cambridge University Press. He won the Best Young Researcher Award of IEEE Communications Society (ComSoc) Asia Pacific (AP) and The 2011 IEEE Communications Society Fred W. Ellersick Prize Paper Award. Currently, he is serving as a senior editor of IEEE Wireless Communications Letter, an area editor of IEEE Transactions on Wireless Communications (Radio Management and Multiple Access), an area editor of IEEE Communications Surveys and Tutorials (Network and Service Management and Green Communication), an editor of IEEE Transactions on Communications, an associate editor of IEEE Transactions on Mobile Computing, IEEE Transactions on Vehicular Technology, and IEEE Transactions on Cognitive Communications and Networking. He was a guest editor of IEEE Journal on Selected Areas on Communications. He was a Distinguished Lecturer of the IEEE Communications Society for 2016-2017. He was named the 2017-2019 highly cited researcher in computer science. He is a Fellow of IEEE.

TUTORIAL #4
  • "Towards Taming Adversarial Examples: Security Applications Perspectives"
  • David A. Mohaisen
  • University of Central Florida, USA
Abstract

The recent rapid advances in machine and deep learning algorithms have found many applications in the security space, targeting various applications including intrusion detection systems, malware detection, and attribution. Despite their extraordinary superhuman performance in various tasks, machine learning algorithms are prone to adversarial examples, carefully crafted input examples to the machine algorithms that will result in fooling the machine algorithms by, for example, reducing their confidence or even resulting in misclassification. In this talk, we review advances in adversarial machine learning space as it pertain to various application security tasks. We further highlight and review several recent studies to demonstrate the success of adversarial examples on various applications, including website fingerprinting, malicious binaries classification, source code authorship identification, and intrusion detection systems. We discuss various defenses and conclude with open directions.

Biography

David Mohaisen earned his M.Sc. and Ph.D. degrees from the University of Minnesota in 2011 and 2012, respectively. He is currently an Associate Professor at the University of Central Florida, where he directs the Security and Analytics Lab (SEAL). Before joining UCF, he held several posts, in academia and industry; as an Assistant Professor at the University at Buffalo, (Senior) Research Scientist at Verisign Labs, and a Member of the Engineering Staff at the Electronics and Telecommunication Research Institute (ETRI). His research interests fall in the broad areas of networked systems and their security, adversarial machine learning, IoT security, AI security, and blockchain security. Among other services, he is currently an Associate Editor of IEEE Transactions on Mobile Computing and IEEE Transactions on Parallel and Distributed Systems. He is a senior member of ACM (2018) and IEEE (2015).

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