เปรียบเทียบวิธี

	Active Learning Federated Learning	การเรียนรู้แบบกึ่งกำกับดูแล
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2020s	1970s–2006 (formalized)
ผู้ริเริ่ม	Multiple authors (federated active learning emerged ~2020)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
ประเภท	Hybrid paradigm (active querying within distributed training)	Learning paradigm
แหล่งต้นตำรับ	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).	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
ชื่อเรียกอื่น	Federated Active Learning, FAL, Active Federated Learning, distributed active learning	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
ที่เกี่ยวข้อง	6	5
สรุป	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.