Federated Optimization over Bandwidth-Limited Heterogeneous Networks — NSF Award to Yale University (CT, $316,074)
Harnessing the power of data collected from a vast amount of geographically distributed and heterogeneous devices, in a manner without moving data around and violating privacy, has great potential in advancing science and technology and improving quality of life. Federated optimization lies at the heart of the practice
| Award title | Federated Optimization over Bandwidth-Limited Heterogeneous Networks |
|---|---|
| Award ID | 2537189 |
| Awardee | Yale University |
| City | NEW HAVEN |
| State | CT |
| Amount obligated | $316,074 |
| Principal investigator | Yuejie Chi |
| Program | CSCS: Circuits and Systems for |
| Start date | 07/01/2025 |
| Abstract | Harnessing the power of data collected from a vast amount of geographically distributed and heterogeneous devices, in a manner without moving data around and violating privacy, has great potential in advancing science and technology and improving quality of life. Federated optimization lies at the heart of the practice realizing this vision, encompassing problems such as training large-scale machine learning or artificial intelligence models, delivering insightful data analytics, as well as faci |
| Source | NSF Awards |
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