Linganisha mbinu

	Kujifundisha kwa Kujitegemea kwa Njia ya Shirikishi	Federated Learning
Nyanja	Ujifunzaji wa Mashine	Faragha
Familia	Machine learning	Machine learning
Mwaka wa asili	2021–2022	2017
Mwanzilishi	McMahan et al. (federated); Zhuang et al. and others (federated SSL combination)	McMahan et al.
Aina	Federated self-supervised pretraining paradigm	Distributed privacy-preserving machine learning
Chanzo asilia	Zhuang, W., Wen, Y., & Zhang, S. (2021). Divergence-aware Federated Self-Supervised Learning. In International Conference on Learning Representations (ICLR 2022).	McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282.
Majina mbadala	FedSSL, Federated Self-supervised Learning, Federated Contrastive Learning, Self-supervised Federated Pretraining	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
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.	Federated Learning is a distributed machine learning paradigm introduced by McMahan et al. in 2017 in which a global model is trained collaboratively across multiple decentralized clients — such as mobile devices or hospital systems — without ever transferring raw data to a central server. Each participant computes model updates locally using its private data; only those updates, not the underlying data, are communicated and aggregated by the server to improve the shared model.