Salīdzināt metodes

	Federated apvienojums (Ensemble Federated Learning)	Pārneses apmācība
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2017–2019	2010 (formalized); 1990s (early roots)
Autors	McMahan et al. (FedAvg) extended by subsequent ensemble work	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Tips	Ensemble meta-strategy over federated clients	Learning paradigm
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.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Citi nosaukumi	federated ensemble learning, EFL, federated model ensembling, federated multi-model aggregation	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Saistītās	6	3
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.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.