Zhao, Xiaochuan, (University of California, Los Angeles) “Learning under Imperfections by Networked Agents” (2015)

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.

10 years of news and resources for members of the IEEE Signal Processing Society

Zhao, Xiaochuan, (University of California, Los Angeles) “Learning under Imperfections by Networked Agents” (2015)

Zhao, Xiaochuan, (University of California, Los Angeles) “Learning under Imperfections by Networked Agents” (2015)  Advisor: Ali H. Sayed

Distributed learning deals with the problem of optimizing aggregate cost functions by networked agents from streaming data. This scenario arises in many contexts including distributed estimation, machine learning, resource allocation, and in the modeling of flocking and swarming behavior by biological networks. Among several available solutions such as consensus and incremental strategies, the class of diffusion strategies has proven to be particularly attractive because these techniques are scalable, robust, fully-distributed, and endow networks with real-time adaptation and learning abilities.

One key challenge in real applications is that networked agents generally face many types of asynchronous imperfections, such as random link failures, random data arrival times, noisy links, random topology changes, agents turning on and off randomly, and even drifting objectives. This dissertation provides a detailed analysis of the stability and performance of asynchronous diffusion strategies for solving distributed optimization and adaptation problems over networks in the presence of such imperfections. Conditions are developed to ensure the stability of the mean-square and mean-fourth-order moments of the network error vectors; closed-form expressions are derived to reveal how the network parameters influence the learning behavior; and the performance of the asynchronous networks is then compared against centralized solutions and synchronous networks. One notable conclusion is that the mean-square performance of asynchronous networks degrades only in the order of μ, which is a small step-size parameter, while the convergence rate remains largely unaltered. A second notable conclusion is that even under the influence of asynchronous events, all agents in the adaptive network can still reach an O (μ1+γ ) near-agreement with some constant γ > 0, while approaching the desired solution within O(μ) accuracy. These theoretical results provide a solid justification for the remarkable resilience of cooperative networks in the face of random imperfections at multiple levels: agents, links, data arrivals, and topology.

The dissertation also examines a second challenging form of uncertainty arising from agents in a network pursuing different objectives or observing data arising from different unknown models. In these cases, indiscriminate cooperation will lead to undesired results. A useful adaptive clustering and learning strategy is developed in order to allow agents to learn which neighbors should be trusted and which other neighbors should be ignored. The resulting procedure enables agents to identify their grouping and to attain improved learning performance.

For details, please visit the thesis page.

Table of Contents:

Research Opportunities

SPS on Twitter

  • NEW WEBINAR: Join us on Friday, 14 August at 11:00 AM ET for the 2021 SPS Membership Preview! Society leadership wi… https://t.co/1PLaZIt2VQ
  • CALL FOR PAPERS: The 2020 IEEE Workshop on Spoken Language Technology is now accepting papers for its January 2021… https://t.co/48604jm3zc
  • CALL FOR PAPERS: The 2020 IEEE International Workshop on Information Forensics and Security is now accepting submis… https://t.co/p9q7UvKgmT
  • CALL FOR CHALLENGES: ISBI 2021 is now accepting proposals for scientific challenges in preparation for their April… https://t.co/W30mmOcGtU
  • The SPACE Webinar Series continues tomorrow, Tuesday, 14 July at 11 AM ET, with Jong Chul Ye presenting "Optimal tr… https://t.co/BYbN2LaecE

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar