ERI: Back Propagation-Free Machine Learning for Split Neural Networks in Distributed Edge — NSF Award to Montclair State Universit
This NSF ERI project aims to make collaborative machine learning more practical for real-world edge systems where data are distributed across devices, and networks often differ in speed, reliability, and computing capability. Today, many distributed learning methods require each device to train a full neural network or
| Award title | ERI: Back Propagation-Free Machine Learning for Split Neural Networks in Distributed Edge |
|---|---|
| Award ID | 2552997 |
| Awardee | Montclair State University |
| City | MONTCLAIR |
| State | NJ |
| Amount obligated | $199,494 |
| Principal investigator | Chao Huang |
| Program | ERI-Eng. Research Initiation |
| Start date | 06/01/2026 |
| Abstract | This NSF ERI project aims to make collaborative machine learning more practical for real-world edge systems where data are distributed across devices, and networks often differ in speed, reliability, and computing capability. Today, many distributed learning methods require each device to train a full neural network or to exchange large amount of information during training, which can be costly for edge devices such as wearables, mobile devices, and other resource-limited platforms. The project |
| Source | NSF Awards |
$799/mo
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