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| 联邦学习× | k-匿名化：保护发布数据中的个体隐私× | |
|---|---|---|
| 领域 | 隐私 | 隐私 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017 | 2002 |
| 提出者≠ | McMahan et al. | Latanya Sweeney |
| 类型≠ | Distributed privacy-preserving machine learning | Privacy-preserving data transformation |
| 开创性文献≠ | 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. link ↗ | Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557–570. DOI ↗ |
| 别名 | Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme | k-Anonymization, k-Anonymous Microdata, Quasi-Identifier Suppression Model, k-Anonimlik |
| 相关≠ | 3 | 2 |
| 摘要≠ | 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. |
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