Linganisha mbinu

	Active Learning Federated Learning	Jifunze kwa Kujisimamia
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2020s	2018–2020
Mwanzilishi	Multiple authors (federated active learning emerged ~2020)	LeCun, Y. and community (formalized ~2018–2020)
Aina	Hybrid paradigm (active querying within distributed training)	Representation 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).	LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/
Majina mbadala	Federated Active Learning, FAL, Active Federated Learning, distributed active learning	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
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.	Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.