Linganisha mbinu

	Active Learning Federated Learning	Kujifunza kwa uhamishaji
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2020s	2010 (formalized); 1990s (early roots)
Mwanzilishi	Multiple authors (federated active learning emerged ~2020)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Aina	Hybrid paradigm (active querying within distributed training)	Learning paradigm
Chanzo asilia	Ro, J. Y., Ali, A., Lin, Z., & Suresh, A. T. (2021). Scaling Federated Learning for Fine-tuning of Large Language Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP).	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Majina mbadala	Federated Active Learning, FAL, Active Federated Learning, distributed active learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Zinazohusiana	6	3
Muhtasari	Federated Active Learning combines the annotation-efficiency of active learning with the privacy-preserving decentralization of federated learning. A shared global model is trained across distributed clients, each of which independently ranks its unlabeled local data and requests labels only for the most informative examples, keeping raw data on-device throughout.	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.