Сравнение на методи

	Онлайн федеративно обучение	Диференциална поверителност
Област	Машинно обучение	Поверителност
Семейство	Machine learning	Machine learning
Година на възникване	2019–2021	2006
Създател	McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021	Cynthia Dwork
Тип	Distributed sequential learning	Privacy-preserving randomized mechanism
Основополагащ източник	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.
Други названия	OFL, federated online learning, streaming federated learning, real-time federated learning	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik
Свързани	5	3
Резюме	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.