Salīdzināt metodes

	Federated apvienojums (Ensemble Federated Learning)	Pastiprināšana
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2017–2019	1990–1997
Autors	McMahan et al. (FedAvg) extended by subsequent ensemble work	Schapire, R. E.; Freund, Y.
Tips	Ensemble meta-strategy over federated clients	Sequential ensemble (iterative reweighting)
Pirmavots	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.	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.
Citi nosaukumi	federated ensemble learning, EFL, federated model ensembling, federated multi-model aggregation	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Saistītās	6	6
Kopsavilkums	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.