| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
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| Initial Amendment Date: | June 22, 2017 |
| Latest Amendment Date: | June 22, 2017 |
| Award Number: | 1704624 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Phillip Regalia
pregalia@nsf.gov (703)292-2981 CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | August 1, 2017 |
| End Date: | July 31, 2021 (Estimated) |
| Total Intended Award Amount: | $451,330.00 |
| Total Awarded Amount to Date: | $451,330.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
450 JANE STANFORD WAY STANFORD CA US 94305-2004 (650)723-2300 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
3160 Porter Drive Palo Alto CA US 94304-1212 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Comm & Information Foundations |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
The proliferation of communication networks means that their efficiency and security now affects almost all aspects of our daily lives. At the societal level, reliable and efficient networks are of central importance to technological development, national economic growth and national defense. In light of this, the problem of understanding fundamental information limits of networked devices is more acute now than ever. Unfortunately, it has been a longstanding challenge in information theory to systematically extend its success beyond classical point-to-point exchanges of information. Despite significant research effort since the inception of the field in 1948, the information limits for most network settings, even for networks as fundamental as a three node relay channel, have remained open to date.
This project aims to change the way many network problems are analyzed today in information theory. Traditional information-theoretic approaches for quantifying fundamental performance limits of networks often fail because the current toolset of information theory is not sufficiently refined to characterize tensions that emerge in networks due to competing objectives. Looking beyond traditional methods, this project will study the role that inequalities from geometric and functional analysis can play in characterizing tensions between information measures. The research project will be complemented with a strong inter-university education program, which includes joint and collaborative student and post-doctoral researcher advising.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
From cellular networks, to the IoT and the Internet, the reliability and efficiency of communication networks now affect almost all aspects of our daily lives. The nodes in these networks exchange information with each other to accomplish tasks such as communication, estimation and learning. What are the fundamental laws that govern information flow in networks and how can a desired task be achieved most efficiently? This project answered such questions by developing new mathematical tools which emphasize a viewpoint from high-dimensional geometry and applying them to communication networks.
There were many new scientific results that were discovered and published during the project, with details in the publications as well as summarized in annual reports of the project. We highlight two major directions:
1) The project led to the solution of a central open problem in network information theory that has remained open for 30 years. This problem concerns the capacity of a three node communication network called a relay channel. Our solution relied on combining measure concentration with a probabilistic geometric analysis of typical sets, which allowed us to establish new and surprising relations between the information measures involved in the problem. We have also shown that tools from geometric and functional analysis, such as reverse hypercontractivity and optimal transport, can be successfully used to prove impossibility results for network information problems.
2) We considered a collection of networked statistical estimation problems modeling bandwidth and privacy constraints in distributed and federated learning systems, and showed that the functional and geometric analysis techniques developed in the project can be successfully used to analyse these systems. In these problems, data is distributed across many nodes in a network and must be communicated to a centralized estimator under communication, privacy, or mutual information constraints. We showed how a geometric interpretation of Fisher information from the processed statistical samples can derive tight minimax lower bounds for many distributed estimation problems of interest.
The research was complemented with a strong cirriculum development effort at the home institution that focused on integrating education and research at the undegraduate level, and enhancing and promoting an understanding of the science of information among a diverse set of undegraduate students accross campus.
Last Modified: 06/08/2022
Modified by: Ayfer Ozgur
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