Comparar métodos

	Aprendizaje Federado en Línea	Privacidad diferencial
Campo	Aprendizaje automático	Privacidad
Familia	Machine learning	Machine learning
Año de origen	2019–2021	2006
Autor original	McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021	Cynthia Dwork
Tipo	Distributed sequential learning	Privacy-preserving randomized mechanism
Fuente seminal	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.
Alias	OFL, federated online learning, streaming federated learning, real-time federated learning	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik
Relacionados	5	3
Resumen	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.