Virtual Distinguished Lecture - Communication Efficient Privacy-Preserving Distributed Optimization
Advanced Registration Required. Event begins at 4 pm Netherlands time -- this is 7 am pacific time.
Your local chapters of AESS, SPS, and OES are joining together to enhance your local San Diego technical community.
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.
This virtual event has been organized by the IEEE Signal Processing Society Information and Forensics Security Technical Committee (SPS-IFS-TC) and fits well with the technical interests of the IEEE Aerospace & Electronics Systems Society Cyber Technical Panel.
You can view virtual events in this series at https://signalprocessingsociety.org/tags/sps-webinar-series.
Please note that this meeting starts at 7AM pacific time, as it's coming from The Netherlands.
Date and Time
- Date: 29 Jun 2022
- Time: 04:00 PM to 05:20 PM
- All times are (UTC+01:00) Amsterdam
- Add Event to Calendar
This is a virtual event over Zoom. Free advanced registration is available at the external registration link. Registration is limited to the first 100 respondents.
Richard Heusdens of Delft University of Technology
Communication Efficient Privacy-Preserving Distributed Optimization
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.