Linganisha mbinu

	Ujifunzaji Unganishi wa Mtandaoni	Federated Learning
Nyanja	Ujifunzaji wa Mashine	Faragha
Familia	Machine learning	Machine learning
Mwaka wa asili	2019–2021	2017
Mwanzilishi	McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021	McMahan et al.
Aina	Distributed sequential learning	Distributed privacy-preserving machine learning
Chanzo asilia	Damaskinos, G., Guerraoui, R., Kermarrec, A.-M., Guirguis, A., Riviere, M., & Tempo, R. (2020). FLEET: Flexible and Efficient Federated Learning for Edge AI. Proceedings of Machine Learning and Systems (MLSys).	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	OFL, federated online learning, streaming federated learning, real-time federated learning	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Zinazohusiana	5	3
Muhtasari	Online Federated Learning (OFL) combines the privacy-preserving, decentralised structure of federated learning with the sequential, sample-by-sample update regime of online learning. Clients — such as mobile devices or edge sensors — receive a global model, update it on newly arriving local data without sharing raw observations, and contribute compressed updates to a central server that aggregates them in near-real-time.	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.