SPS IFS TC Webinar: 29 June 2022, by Dr. Richard Heusdens

You are here

Inside Signal Processing Newsletter Home Page

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

News and Resources for Members of the IEEE Signal Processing Society

SPS IFS TC Webinar: 29 June 2022, by Dr. Richard Heusdens

IEEE SPS IFS TC Webinar
(Organized by the IEEE SPS Information Forensics and Security Technical Committee (IFS TC))

Title: Communication Efficient Privacy-Preserving Distributed Optimization
Date: 29 June 2022
Time: 4:00 PM (Amsterdam, The Netherlands time) Your local time | Add to calendar
Duration: Approximately 1 Hour
Presenters: Dr. Richard Heusdens

 

Register for the webinar to acquire Zoom link!

About the topic:

Privacy issues and communication costs are both major concerns in distributed optimization in networks. There is often a tradeoff between them because encryption methods used for privacy-preservation often introduce significant communication overhead. In this talk, we discuss a quantization-based approach to achieve both communication efficiency and privacy-preserving in the context of distributed optimization. By deploying an adaptive differential quantization scheme, we allow each node in the network to achieve the optimum solution with low communication costs while keeping its private data unrevealed. The proposed approach is general and can be applied in various distributed optimization methods, such as dual ascent and methods based on operator splitting (PDMM and ADMM). We consider two widely used adversary models, passive and eavesdropping, and investigate the properties of the proposed approach using different applications and demonstrate its superior performance compared to existing privacy-preserving approaches in terms of privacy, accuracy, and communication cost.


About the presenter:

Richard Heusdens

Richard Heusdens received the M.Sc. and Ph.D. degrees from Delft University of Technology, Delft, The Netherlands, in 1992 and 1997, respectively.

Since 2019, he has been a full professor at the Netherlands Defence Academy and guest professor at Delft University of Technology. He is involved in research projects that cover subjects such as audio and acoustic signal processing, sensor signal processing, distributed optimization, and security/privacy. In spring 1992, he joined the digital signal processing group at the Philips Research Laboratories, Eindhoven, The Netherlands. He has worked on various topics in the field of signal processing, such as image/video compression and VLSI architectures for image processing algorithms. In 1997, he joined the Circuits and Systems Group of Delft University of Technology, where he was a postdoctoral researcher. In 2000, he moved to the Information and Communication Theory (ICT) Group, where he became an assistant/associate professor responsible for the audio/speech signal processing activities. He held visiting positions at KTH (Royal Institute of Technology, Sweden) in 2002 and 2008, respectively, and was a guest professor at Aalborg University from 2014 to 2016.

SPS Social Media

IEEE SPS Educational Resources

IEEE SPS Resource Center

IEEE SPS YouTube Channel