Methoden vergleichen
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| Ensemble Federated Learning× | Boosting× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2017–2019 | 1990–1997 |
| Urheber≠ | McMahan et al. (FedAvg) extended by subsequent ensemble work | Schapire, R. E.; Freund, Y. |
| Typ≠ | Ensemble meta-strategy over federated clients | Sequential ensemble (iterative reweighting) |
| Wegweisende Quelle≠ | McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 54, 1273–1282. link ↗ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ |
| Aliasnamen | federated ensemble learning, EFL, federated model ensembling, federated multi-model aggregation | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Ensemble Federated Learning combines the privacy-preserving distribution of federated learning with ensemble aggregation: each participating client trains its own local model on private data, and the server aggregates predictions — or model parameters — from all clients using ensemble strategies such as voting, averaging, or stacking, instead of simple parameter averaging alone. | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. |
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