Сравнение методов

	Федеративное обучение	Дифференциальная приватность
Область	Конфиденциальность	Конфиденциальность
Семейство	Machine learning	Machine learning
Год появления	2017	2006
Автор метода	McMahan et al.	Cynthia Dwork
Тип	Distributed privacy-preserving machine learning	Privacy-preserving randomized mechanism
Основополагающий источник	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.	Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12.
Другие названия	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik
Связанные	3	3
Сводка	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.	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.