PDaSP Track 3: Testbed for Enhancing Privacy and Robustness of Federated Learning Systems — NSF Award to University of Minnesota-T
Traditional machine learning often involves collecting data from multiple sources, which can raise significant privacy concerns. One approach has emerged as a promising solution to solve this challenge by enabling models to be trained across many different sources without directly sharing private data. This approach ha
| Award title | PDaSP Track 3: Testbed for Enhancing Privacy and Robustness of Federated Learning Systems |
|---|---|
| Award ID | 2452817 |
| Awardee | University of Minnesota-Twin Cities |
| City | MINNEAPOLIS |
| State | MN |
| Amount obligated | $119,876 |
| Principal investigator | Ali Anwar |
| Program | Privacy Preserving Data Sharin |
| Start date | 10/01/2025 |
| Abstract | Traditional machine learning often involves collecting data from multiple sources, which can raise significant privacy concerns. One approach has emerged as a promising solution to solve this challenge by enabling models to be trained across many different sources without directly sharing private data. This approach has become particularly valuable in sensitive sectors such as medical diagnostics, where individual data privacy is legally protected. Despite these advancements, existing systems fo |
| Source | NSF Awards |
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