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异质分布式隐私约束下非参数回归的最优联邦学习AI 解读

This paper investigates federated learning for nonparametric regression under heterogeneous differential privacy (DP) constraints. Data is distributed across multiple servers with varying sample sizes and privacy budgets, with each server communicating privatized summaries to a central aggregator. Our work operates within the Federated Differential Privacy (FDP) framework, which generalizes both local and central DP frameworks.We establish the minimax optimal rates of…

异质隐私约束下函数均值估计的最优联邦学习AI 解读

Abstract Federated learning (FL) is a distributed machine learning technique designed to preserve data privacy and security, and it has gained significant importance due to its broad range of applications. This paper addresses the problem of optimal functional mean estimation from discretely sampled data in a federated setting. We consider a heterogeneous framework where the number of individuals, measurements per individual, and privacy parameters vary across one or more…

分布式差分隐私约束下非参数分类的极小极大与自适应迁移学习AI 解读

Abstract This paper considers minimax and adaptive transfer learning for nonparametric classification under the posterior drift model with distributed differential privacy constraints. Our study is conducted within a heterogeneous framework, encompassing diverse sample sizes, varying privacy parameters, and data heterogeneity across different servers. We first establish the minimax misclassification rate, precisely characterizing the effects of privacy constraints, source…

基于成对比较的最优差分隐私排序AI 解读

Data privacy is a central concern in many applications involving ranking from incomplete and noisy pairwise comparisons, such as recommendation systems, educational assessments, and opinion surveys on sensitive topics. In this work, we propose differentially private algorithms for ranking based on pairwise comparisons. Specifically, we develop and analyze ranking methods under two privacy notions: edge differential privacy, which protects the confidentiality of individual…

协调 model-X 与双重稳健条件独立检验AI 解读

Model-X approaches to testing conditional independence between a predictor and an outcome variable given a vector of covariates usually assume exact knowledge of the conditional distribution of the predictor given the covariates. Nevertheless, model-X methodologies are often deployed with this conditional distribution learned in sample. We investigate the consequences of this choice through the lens of the distilled conditional randomization test (dCRT). We find that Type-I…