手法を比較

	オンライン連合学習	転移学習
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2019–2021	2010 (formalized); 1990s (early roots)
提唱者	McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
種類	Distributed sequential learning	Learning paradigm
原典	Damaskinos, G., Guerraoui, R., Kermarrec, A.-M., Guirguis, A., Riviere, M., & Tempo, R. (2020). FLEET: Flexible and Efficient Federated Learning for Edge AI. Proceedings of Machine Learning and Systems (MLSys).	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
別名	OFL, federated online learning, streaming federated learning, real-time federated learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
関連	5	3
概要	Online Federated Learning (OFL) combines the privacy-preserving, decentralised structure of federated learning with the sequential, sample-by-sample update regime of online learning. Clients — such as mobile devices or edge sensors — receive a global model, update it on newly arriving local data without sharing raw observations, and contribute compressed updates to a central server that aggregates them in near-real-time.	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.