পদ্ধতির তুলনা করুন

	ফেডারেটেড লার্নিং	কে-অ্যানোনিমিটি: প্রকাশিত তথ্যে ব্যক্তিগত গোপনীয়তা সুরক্ষা	প্রকাশনা নিয়ন্ত্রণের জন্য সিন্থেটিক ডেটা তৈরি
ক্ষেত্র	গোপনীয়তা	গোপনীয়তা	গোপনীয়তা
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	2017	2002	1993
প্রবর্তক	McMahan et al.	Latanya Sweeney	Donald Rubin
ধরন	Distributed privacy-preserving machine learning	Privacy-preserving data transformation	Privacy-preserving data synthesis
মৌলিক উৎস	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.	Rubin, D. B. (1993). Statistical disclosure limitation. Journal of Official Statistics, 9(2), 461–468.
অপর নাম	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	k-Anonymization, k-Anonymous Microdata, Quasi-Identifier Suppression Model, k-Anonimlik	Fully Synthetic Data, Partial Synthetic Data, Statistical Data Synthesis, Sentetik Veri Üretimi
সম্পর্কিত	3	2	3
সারসংক্ষেপ	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.	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.