| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | May 4, 2021 |
| Latest Amendment Date: | May 4, 2021 |
| Award Number: | 2105004 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Marilyn McClure
mmcclure@nsf.gov (703)292-5197 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | July 1, 2021 |
| End Date: | May 31, 2024 (Estimated) |
| Total Intended Award Amount: | $174,823.00 |
| Total Awarded Amount to Date: | $174,823.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
107 S INDIANA AVE BLOOMINGTON IN US 47405-7000 (317)278-3473 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
723 W. Michigan St., SL 280 Indianapolis IN US 46202-5191 |
| 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): | CSR-Computer Systems Research |
| 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
Facilitated by multiple sensors on mobile devices, mobile crowd sensing (MCS) relies on the mobility and characteristics of mobile users to perceive the physical world in real time, inspiring massive innovative services. Despite its prevailing deployment and great potential, conventional MCS devices transmit all sensing data to the requestors, overburdening them with high communication and computation resource consumption. This becomes even worse in practice since redundant workers are recruited for quality consideration, thus offsetting the major advantage of economical monitoring and frustrating resource-constrained requestors.
This project seeks to integrate sensing and learning for MCS without the consumption of excessive resources. Specifically, given that sensing data is being collected through dispersed edge servers, blockchain-based federated learning (FL) is introduced to protect data privacy and achieve distributed machine learning (ML) with performance enhancement of trustworthiness and efficiency. The technical contributions of this research include the extension of trust from on-chain to off-chain procedures via incentive mechanism designs for eliciting trustworthy submissions from distributed edge learners. It also aims to establish instantly reliable computing environments in an off-chain manner for guaranteed efficiency of distributed ML in MCS, with both the intra-environment consensus protocol design and inter-environment interaction analysis.
The research outcome of this project will contribute to improving the availability and cost-efficiency of MCS, making the perception of the physical world more economical and intelligent. The main technology of blockchain-based distributed ML can benefit society by enhancing applications involving temporal-spatial data collection and calculation. Success of this project will enhance things such as internet of things (IoT), smart cities, wireless networking, and more. The research will be closely integrated into the education and training of students while advancing curriculum development with new theories and methodologies. This project plans to inspire future generations of diverse researchers to join science and engineering. Rapid dissemination of research findings will be realized through publications to top conferences and journals. All designs will be publicly available on the PIs website for broad adoption and future research advances.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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.
This project explored how federated learning and blockchain technologies can be integrated to improve mobile crowd sensing applications. Our work spanned theoretical research, algorithm design, and practical implementation, resulting in significant outcomes in five key aspects:
1. Incentive and Resource Allocation Mechanisms: We developed incentive schemes and resource allocation methods for edge servers, which are crucial participants in the blockchain-based federated learning framework. These mechanisms ensure that servers are fairly compensated and resources are optimally utilized, making the system more efficient.
2. System Stability and Security: To improve the stability and sustainability of federated learning, we identified the optimal set of participants and maximized their contributions of local training data. Our approach enhances social welfare while also safeguarding the system from malicious attacks.
3. Real-World Applications: We applied the blockchain-based federated learning framework to various practical applications, including mobile crowd sensing, smart homes, smart cities, and the metaverse. These applications demonstrated how the framework provides secure, efficient, and reliable services across different environments.
4. Advances in Game Theory: Game theory played a central role in achieving our results. We extended the zero-determinant strategy to multi-play, multi-action scenarios and developed a new collective extortion strategy, contributing to the broader understanding of game theory in decentralized systems.
5. Educational Impact: This project supported two PhD students over the course of two years, with the research forming a major component of their dissertations. Additionally, the findings have been incorporated into two courses at IUPUI, benefiting both graduate and undergraduate students by exposing them to the latest developments in blockchain and federated learning.
In summary, this project has advanced both the theoretical foundations and practical applications of federated learning and blockchain technologies, with impacts on research, education, and real-world implementation.
Last Modified: 09/06/2024
Modified by: Qin Hu
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