Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning
Federated Learning (FL) has been proposed as a privacy-preserving solution for distributed machine learning, particularly in heterogeneous FL settings where clients have varying computational capabilities and thus train models with different complexities compared to the server’s model. Howeve...
Zapisane w:
| Główni autorzy: | , , , |
|---|---|
| Format: | Artigo |
| Język: | Inglês |
| Wydane: |
IEEE
2025-01-01
|
| Seria: | IEEE Access |
| Hasła przedmiotowe: | |
| Dostęp online: | https://ieeexplore.ieee.org/document/10906485/ |
| Etykiety: |
Nie ma etykietki, Dołącz pierwszą etykiete!
|