Linganisha mbinu

	Kujifundisha kwa Kujitegemea kwa Njia ya Shirikishi	Jifunze kwa Kujisimamia
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2021–2022	2018–2020
Mwanzilishi	McMahan et al. (federated); Zhuang et al. and others (federated SSL combination)	LeCun, Y. and community (formalized ~2018–2020)
Aina	Federated self-supervised pretraining paradigm	Representation learning paradigm
Chanzo asilia	Zhuang, W., Wen, Y., & Zhang, S. (2021). Divergence-aware Federated Self-Supervised Learning. In International Conference on Learning Representations (ICLR 2022).	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	FedSSL, Federated Self-supervised Learning, Federated Contrastive Learning, Self-supervised Federated Pretraining	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Zinazohusiana	5	3
Muhtasari	Self-supervised Federated Learning combines federated training — where data never leaves local devices — with self-supervised pretext tasks such as contrastive learning or masked prediction. Clients learn general-purpose representations from their own unlabeled data and share only model updates, not raw data, with a central server that aggregates them into a global encoder.	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.