השוואת שיטות

	למידה מאוחדת (Federated Learning)	יצירת נתונים סינתטיים לבקרת חשיפה
תחום	פרטיות	פרטיות
משפחה	Machine learning	Machine learning
שנת המקור	2017	1993
הוגה השיטה	McMahan et al.	Donald Rubin
סוג	Distributed privacy-preserving machine learning	Privacy-preserving data synthesis
מקור מכונן	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.	Rubin, D. B. (1993). Statistical disclosure limitation. Journal of Official Statistics, 9(2), 461–468.
כינויים	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	Fully Synthetic Data, Partial Synthetic Data, Statistical Data Synthesis, Sentetik Veri Üretimi
קשורות	3	3
תקציר	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.	Synthetic data generation is a statistical disclosure limitation technique introduced by Donald Rubin in 1993, in which values in a confidential dataset are replaced by draws from a fitted posterior predictive distribution rather than released directly. The resulting artificial records preserve the joint statistical structure of the original data while preventing the identification of real individuals, enabling analysts to work with a publicly releasable dataset that behaves like the original for most inferential purposes.