So sánh phương pháp

	Học liên kết	k-Anonymity: Bảo vệ Quyền riêng tư Cá nhân trong Dữ liệu Được Công bố
Lĩnh vực	Quyền riêng tư	Quyền riêng tư
Họ	Machine learning	Machine learning
Năm ra đời	2017	2002
Người khởi xướng	McMahan et al.	Latanya Sweeney
Loại	Distributed privacy-preserving machine learning	Privacy-preserving data transformation
Công trình gốc	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.
Tên gọi khác	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	k-Anonymization, k-Anonymous Microdata, Quasi-Identifier Suppression Model, k-Anonimlik
Liên quan	3	2
Tóm tắt	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.