Sammenlign metoder

	Fødereret læring	k-Anonymitet: Beskyttelse af individuel privatliv i frigivne data
Fagområde	Privatlivsbeskyttelse	Privatlivsbeskyttelse
Familie	Machine learning	Machine learning
Oprindelsesår	2017	2002
Ophavsperson	McMahan et al.	Latanya Sweeney
Type	Distributed privacy-preserving machine learning	Privacy-preserving data transformation
Oprindelig kilde	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.	Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557–570.
Aliasser	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	k-Anonymization, k-Anonymous Microdata, Quasi-Identifier Suppression Model, k-Anonimlik
Relaterede	3	2
Resumé	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.	k-Anonymity is a formal privacy model introduced by Latanya Sweeney in 2002 to protect individuals when personal data is released for research or public use. It requires that every record in a published dataset be indistinguishable from at least k−1 other records with respect to a designated set of quasi-identifying attributes — such as age, gender, and ZIP code — preventing re-identification by linking released data to external sources.