Ensemble Federated Learning
Ensemble Federated Learning combineert de privacy-behoudende distributie van federated learning met ensemble-aggregatie: elke deelnemende client traint zijn eigen lokale model op private data, en de server aggregeert voorspellingen — of modelparameters — van alle clients met behulp van ensemble-strategieën zoals stemming, middeling, of stacking, in plaats van alleen simpele parametergemiddelden.
Lees de volledige methode
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Method map
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Bronnen
- 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 ↗
Deze pagina citeren
ScholarGate. (2026, June 3). Ensemble Federated Learning (Federated Ensemble Aggregation). ScholarGate. https://scholargate.app/nl/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)Machine learning↔ compare
- BoostingMachine learning↔ compare
- Federated LearningPrivacy↔ compare
- StackingMachine learning↔ compare
- TransferlerenMachine learning↔ compare
- Voting EnsembleMachine learning↔ compare
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