Project Grant 2340482
- This four-year $300,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop secure foundations for federated learning. Federated learning enables machine learning models to be collaboratively trained using data from many client devices without sharing private information. The researchers will investigate security vulnerabilities in federated learning's training phase, such as poisoning and backdoor attacks. They will...
- This National Science Foundation project grant of $599,999 will fund research at Duke University from October 2022 through September 2026 towards developing secure methods for federated learning. Federated learning is an emerging machine learning technique that allows analysis of private data without centralized collection, but current methods lack security protections. Under the Computer and Information Science and Engineering program (CFDA 47.070), the researchers will explore new security...
- The National Science Foundation awarded a $300,000 project grant to Princeton University under the Computer and Information Science and Engineering program to support research towards securing federated learning. Over a four-year period ending September 2026, Princeton researchers will investigate security vulnerabilities in the training phase of federated learning models, develop provably secure federated learning methods to prevent poisoning and backdoor attacks, and create techniques to...
- This $348,573 project grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering federal grant program (CFDA 47.041), will fund research at North Carolina State University from September 2022 through August 2025. The university will advance the frontiers of federated learning through exploring tradeoffs among learning performance, communication efficiency, privacy protection, and system robustness under a generalized...
- This $173,754 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop an innovative privacy-preserving federated learning (FL) framework suitable for heterogeneous edge devices. The key objectives are to: 1) enable tailored device-specific models to mitigate biases and enhance performance across diverse computational capabilities and data distributions, 2) utilize differential privacy...
- This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $199,999 to San Francisco State University to develop a resilient next-generation (NextG) network design for federated learning over mobile devices. The key products and services to be delivered under this 2-year award include: Exploiting serverless computing at the network edge to efficiently provide machine learning computing...
- Lehigh University received a $175,000 Project Grant award from the National Science Foundation under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to conduct research towards improving the handling of heterogeneity and personalization in federated learning. The University will develop mathematical models and efficient algorithms to address issues in heterogeneous federated learning caused by data and device diversity. Researchers will design advanced...
- This federal Project Grant, awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to design and develop a secure and efficient decentralized federated learning (DFL) system. The $380,667 grant, awarded on August 15, 2024, will fund research to address communication, computation, and security issues in DFL, which enables training of data-hungry machine learning models on local devices without sharing raw data. The...
- This $219,332 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program aims to develop a secure and efficient decentralized federated learning (DFL) system. The key products and services to be delivered include: Developing computational theories, models, and prototype systems to establish the foundations for trustworthy DFL, addressing both high-performance accuracy and security with privacy...
- This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering program (CFDA 47.070) provides $170,000 to The Research Foundation For The State University Of New York, doing business as Stony Brook University, from October 1, 2023 to September 30, 2025. The project aims to conduct a comprehensive analysis of the characteristics and vulnerabilities of critical learning periods in federated learning systems in order to advance the study of the...
CAREER: STRENGTHENING THE THEORETICAL FOUNDATIONS OF FEDERATED LEARNING: UTILIZING UNDERLYING DATA STATISTICS IN MITIGATING HETEROGENEITY AND CLIENT FAULTS -REAL-WORLD APPLICATIONS THAT WOULD BENEFIT FROM IMPROVED MACHINE LEARNING ENCOMPASS A WIDE RANGE OF INDUSTRIES AND DOMAINS SUCH AS HEALTHCARE, AUTONOMOUS VEHICLES, NATURAL LANGUAGE PROCESSING, AND MANUFACTURING AND INDUSTRY. DISTRIBUTED MACHINE LEARNING HAS GAINED SIGNIFICANT MOMENTUM IN RECENT YEARS DUE TO THE INCREASING NEED FOR REAL-TIME DATA PROCESSING, LOW LATENCY, AND PRIVACY CONCERNS. THE RAPID DEVELOPMENT OF EDGE DEVICES BROADENS THE APPLICABILITY OF DISTRIBUTED MACHINE LEARNING YET BRINGS NONTRIVIAL CHALLENGES THAT CALL FOR REVISITING THE FUNDAMENTAL PRINCIPLES AND ALGORITHM DESIGNS FOR FEDERATED LEARNING. THE RESEARCH GOAL OF THIS PROJECT IS TO CONSOLIDATE THE THEORETICAL FOUNDATIONS AND TO ENRICH THE ALGORITHMIC TOOLBOX OF DISTRIBUTED MACHINE LEARNING WITH A FOCUS ON ENHANCING ITS RESILIENCE AGAINST A WIDE RANGE OF DATA HETEROGENEITY, SYSTEM IMPERFECTION (OR FAULTS), AND EXTERNAL ATTACKS. THE EDUCATIONAL OBJECTIVE OF THIS PROJECT IS TO PROMOTE THE IMPORTANCE OF PRINCIPLED MATHEMATICAL THINKING FOR SOLVING REAL-WORLD PROBLEMS IN MACHINE LEARNING AMONG THE NEXT GENERATION OF MACHINE LEARNING PRACTITIONERS AND RESEARCHERS, WITH A FOCUS ON DEVELOPING PROGRAMS THAT TARGET WOMEN AND UNDERREPRESENTED MINORITY GROUPS. FEDERATED LEARNING IS A RAPIDLY EVOLVING DISTRIBUTED MACHINE LEARNING APPROACH THAT FACILITATES GLOBAL MODEL TRAINING WITHOUT THE NECESSITY OF SHARING RAW LOCAL DATA. MOST EXISTING THEORETICAL ANALYSIS OF FEDERATED LEARNING IS DERIVED FROM AN OPTIMIZATION PERSPECTIVE BUT THE UNDERLYING STATISTICAL STRUCTURE OF THE DATASET IS MOSTLY OVERLOOKED. THIS OFTEN LEADS TO MISALIGNMENT BETWEEN THE PESSIMISTIC THEORETICAL PREDICTIONS AND EMPIRICAL SUCCESS. IN ADDITION, RECENT WORK SUGGESTS THAT THE BOUNDED GRADIENT DISSIMILARITY CONDITIONS, WHICH ARE FREQUENTLY ADOPTED IN FEDERATED LEARNING ANALYSIS, MAY BE TOO PESSIMISTIC FOR PRACTICAL APPLICATIONS. MOTIVATED BY OUR PRELIMINARY SUCCESSES AND BACKED BY EXTENSIVE PRIOR WORK, THIS PROPOSAL AIMS TO STRENGTHEN THE THEORETICAL FOUNDATIONS OF FEDERATED LEARNING AND TO ENHANCE ITS RESILIENCE AGAINST A WIDE RANGE OF DATA HETEROGENEITY AND SYSTEM FAILURES, BY LEVERAGING THE UNDERLYING STRUCTURES OF THE FEDERATED DATASETS AND BY DESIGNING NEW ALGORITHMS. TOWARDS THIS GOAL WE WILL EMPLOY AND INNOVATE TOOLS FROM STATISTICAL LEARNING, DISTRIBUTED COMPUTING, HIGH-DIMENSIONAL PROBABILITIES, AND OPTIMIZATION. 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.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $122.0k | 8/24/25 | ||
| Not listed | $117.1k | 5/2/25 | ||
| Not listed | $112.1k | 1/5/24 |