Data-weighted ensemble learning for privacy-preserving distributed learning
Data-weighted ensemble learning for privacy-preserving distributed learning
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DOI：
10.1109/icassp.2016.7472089
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
Liyang Xie;S. Plis;A. Sarwate
中科院分区：
文献类型：
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作者：
Liyang Xie;S. Plis;A. Sarwate
在协作医学研究环境中，适度数量的组(站点)可能希望合并对私人主题数据的本地分析。差异隐私为这些本地分析提供了一种保证隐私的方法。我们描述了一种新的集成学习方法，我们称之为“特征方法”，用于聚集在局部数据上训练的二进制分类器或回归。我们的方法利用聚集器上可用的公共数据集来优化本地预测器的线性组合。我们对该方法进行了一些分析，并展示了当本地站点需要学习区分私有的分类器时，该方法是如何有效的。我们证明了当局部数据集足够大时，在一定的参数要求下，该方法具有接近最优的性能。在实验上，我们给出了特征方法和局部分类器平均的标准方法的比较。
In collaborative medical research settings, a moderate number of groups (sites) may wish to merge local analyses of private subject data. Differential privacy offers one way to guarantee privacy for these local analyses. We describe a novel ensemble learning method that we call the "feature method" for aggregating binary classifiers or regressors trained on local data. Our method leverages a public data set available at the aggregator to optimize a linear combination of local predictors. We provide some analysis of the method and show how it is effective when the local sites are required to learn classifiers that are differentially private. We prove that this method has near-optimal performance when local data sets are large enough under certain requirements on the parameters. Experimentally, we give a comparison of the feature method and the standard approach of averaging the local classifiers.