The Communication Theory Technical Committee (CTTC) of the IEEE Communications Society is primarily interested in fundamental problems associated with the transmission of information. Of special interest is the novel use of communication theory and/or information theory to solve problems in areas that include (but are not limited to) source and channel coding, storage, modulation, detection, estimation, synchronization, multiple access, interference mitigation, and networking. Communications through all media such as wireless media, wireline, fiber, infrared, optical, magnetic storage, etc. are of interest. Applications, such as wired/wireless/hybrid networks, multi-antenna communications, short range acoustical links, long-distance space communications, voice, data, image, and multimedia transmission, and storage channels are included.

Last Meeting: The TC meeting at GC 2023 was held virtually on Thursday, November 16, 2023, 11am EST/4pm UTC.


Announcement: Congratulations to the success of 2023 IEEE Communication Theory Workshop (CTW 2023) in Hualien, Taiwan. The CTW 2024 will be held May 19–22, in Banff, Canada.


Award Announcement: Congratulations to Jia Ye for winning the first Andrea Goldsmith Young Scholars

The CTTC Awards Committee has decided to grant the 2022 Andrea Goldsmith Young Scholars Award award to Jia Ye, due to her thorough and extensive contributions in the multiple areas within communication  theory, such as reconfigurable intelligent surfaces (RIS) and non-terrestrial communications. She has exhibited high productivity, created an impressive publication record during her PhD studies, demonstrated innovativeness through patents, and received multiple accolades.

Jia Ye has been pursuing her Ph.D. studies at KAUST, Saudi Arabia, since 2020, under the supervision of Abla Kammoun and Mohamed-Slim Alouini.


 

Coming up: The IEEE Communication Theory Technical Committee (CTTC) invites you to the “2+1” online event

Title: Federated Learning and Analytics

Date/Time: October 23, 2023 9.00am – 10:30am PT / 4.00pm – 5:30pm UTC / 6.00pm – 7:30pm CEST

To attend the event, register by filling out the following form (registration deadline October 21, 11:59pm PT): LINK

Question Collection Link before the seminar

Detailed Agenda (time in PT)

9:00-9:05 Welcome Speech, by Ayfer Ozgur
9:05-9:25 Invited Talk I by Peter Kairouz
9:30-9:50 Invited Talk II by Deniz Gündüz
9:55-10:30 Panel Discussions and Questions (Including the ones collected already). Moderator: Ayfer Ozgur

Moderator: Ayfer Özgür (Stanford University, USA). Ayfer Özgür is  an Associate Professor with the Department of Electrical Engineering, Stanford University, where she is the Chambers Faculty Scholar with the School of Engineering. Her research interests include information theory, wireless communication, statistics, and machine learning. She received the EPFL Best Ph.D. Thesis Award in 2010, the NSF CAREER Award in 2013, the Okawa Foundation Research Grant, the Faculty Research Awards from Google and Facebook, and the IEEE Communication Theory Technical Committee (CTTC) Early Achievement Award in 2018. She was selected as the Inaugural Goldsmith Lecturer of the IEEE ITSoc in 2020. In 2023, she also received the Stanford University Electrical Engineering Chair’s award for Outstanding Contributions to Undergraduate Education.

Invited Talk I: Federated Learning in Practice: Reflections and Projections, by Peter Kairouz (Google, USA)

Introduced in 2016 as a privacy-enhancing technique, federated learning has made significant strides in recent years. This presentation offers a retrospective view, delving into the foundational principles of federated learning, encompassing the diverse variants and definitions presented in the ‘Advances and Open Problems in Federated Learning’ manuscript. We highlight key milestones, spotlighting the major implementations within the Google ecosystem and explaining the meticulous efforts dedicated to the fusion of federated learning with secure aggregation and formal differential privacy guarantees. We also touch on the nascent trends on the horizon and provide insights into the evolving landscape of federated learning and its definitions. These evolutionary steps are essential to perpetuate its practical impact.

Peter Kairouz is a Staff Research Scientist at Google, where he focuses on researching and building private, secure, and trustworthy AI technologies. Before joining Google, he was a Postdoctoral Research Fellow at Stanford University. He received his Ph.D. in electrical and computer engineering from the University of Illinois at Urbana-Champaign (UIUC). He is the recipient of the 2012 Roberto Padovani Scholarship from Qualcomm’s Research Center, the 2015 ACM SIGMETRICS Best Paper Award, the 2015 Qualcomm Innovation Fellowship Finalist Award, the 2016 Harold L. Olesen Award for Excellence in Undergraduate Teaching from UIUC, and the 2021 ACM Conference on Computer and Communications Security (CCS) Best Paper Award. 

 

 

 

Invited Talk II: Federated Learning over Wireless Networks, by Deniz Gündüz (Imperial College London, UK)

Mobile devices collect massive amounts of data, opening up new potentials for machine learning applications. Federated edge learning (FEEL) can benefit from both the data and processing power distributed across wireless devices, but this brings about many challenges. In this talk, I will focus on the communication aspects of FEEL, particularly focusing on the interference and resource management issues of federated learning among devices sharing the same wireless medium. I will show that FEEL requires a completely fresh look at wireless access and interference management, and highlight novel approaches such as over-the-air computing,  update-aware scheduling, and hierarchical FEEL.

Deniz Gündüz is a Professor of Information Processing in the Electrical and Electronic Engineering Department at Imperial College London, UK, where he leads the Information Processing and Communications Lab (IPC-Lab). His research interests lie in the areas of communications and information theory, machine learning, and privacy. Dr. Gündüz is a Fellow of the IEEE, and an elected member of the IEEE Signal Processing Society Signal Processing for Communications and Networking (SPCOM), and Machine Learning for Signal Processing (MLSP) Technical Committees. He serves in editorial roles for the IEEE Transactions on Information Theory (area editor), IEEE Transactions on Communications (area editor), IEEE Transactions on Wireless Communications (editor), IEEE Journal on Selected Areas in Information Theory (guest editor). He is the recipient of the IEEE Communications Society – Communication Theory Technical Committee (CTTC) Early Achievement Award in 2017, Starting (2016), Consolidator (2022) and Proof-of-Concept (2023) Grants of the European Research Council (ERC), and has co-authored several award-winning papers, including the IEEE Communications Society – Young Author Best Paper Award (2022), and IEEE International Conference on Communications Best Paper Award (2023). In 2023, he also received the Imperial College London – President’s Award for Excellence in Research Supervision.

 
 

 


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