Vertaile menetelmiä

	Differentiaalinen yksityisyys	Hajautettu oppiminen
Tieteenala	Yksityisyydensuoja	Yksityisyydensuoja
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2006	2017
Kehittäjä	Cynthia Dwork	McMahan et al.
Tyyppi	Privacy-preserving randomized mechanism	Distributed privacy-preserving machine learning
Alkuperäislähde	Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12.	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.
Rinnakkaisnimet	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Liittyvät	3	3
Tiivistelmä	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.	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.