Võrdle meetodeid

	Veebipõhine föderatiivne õppimine	Diferentsiaalne privaatsus
Valdkond	Masinõpe	Privaatsus
Perekond	Machine learning	Machine learning
Tekkeaasta	2019–2021	2006
Looja	McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021	Cynthia Dwork
Tüüp	Distributed sequential learning	Privacy-preserving randomized mechanism
Algallikas	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).	Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12.
Rööpnimetused	OFL, federated online learning, streaming federated learning, real-time federated learning	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik
Seotud	5	3
Kokkuvõte	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.	Differential privacy is a mathematical framework for releasing statistical information about a dataset while providing rigorous guarantees that individual records cannot be identified or inferred. Introduced by Cynthia Dwork in 2006, it formalizes privacy as a probabilistic bound: any single individual's presence or absence in the dataset changes the output distribution by at most a multiplicative factor of e^ε, where ε is the privacy budget controlling the privacy–utility tradeoff.