方法对比

	在线联邦学习	差分隐私
领域	机器学习	隐私
方法族	Machine learning	Machine learning
起源年份	2019–2021	2006
提出者	McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021	Cynthia Dwork
类型	Distributed sequential learning	Privacy-preserving randomized mechanism
开创性文献	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.
别名	OFL, federated online learning, streaming federated learning, real-time federated learning	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik
相关	5	3
摘要	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.