Ensemble Federated Learning
Ensemble Federated Learning kombinerer den privatlivsbevarende distribution af federated learning med ensemble-aggregering: hver deltagende klient træner sin egen lokale model på private data, og serveren aggregerer forudsigelser — eller modelparametre — fra alle klienter ved hjælp af ensemble-strategier såsom afstemning, gennemsnit eller stacking, i stedet for blot simpel parametergennemsnit.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- 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 ↗
- Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2021). FedHealth: A federated transfer learning framework for wearable healthcare. IEEE Intelligent Systems, 35(4), 83–93. DOI: 10.1109/MIS.2020.2988604 ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Ensemble Federated Learning (Federated Ensemble Aggregation). ScholarGate. https://scholargate.app/da/machine-learning/ensemble-federated-learning
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Bagging (Bootstrap Aggregating)Maskinlæring↔ compare
- BoostingMaskinlæring↔ compare
- Fødereret læringPrivatlivsbeskyttelse↔ compare
- StackingMaskinlæring↔ compare
- OverførselslæringMaskinlæring↔ compare
- StemmeensembleMaskinlæring↔ compare
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