面向多分类任务的隐私保护与分布鲁棒联邦学习
Private and Distributionally Robust Federated Multiclass Classification
AI总结：
本文提出一种带ADMM算法的差分隐私分布鲁棒联邦多分类框架，为客户端记录提供样本级隐私，在工业故障诊断等任务中优于相关基线方法。
摘要：
资产密集型行业的故障诊断需要基于地理分布式客户端站点收集的含噪、敏感数据训练多分类模型。联邦学习支持在不集中数据的情况下进行协作训练，但大多数方法未对客户端存储的单个记录提供正式隐私保证，且在标签噪声下性能会下降。为解决这些局限，本文开发了一种用于联邦多分类的差分隐私、分布鲁棒框架，为每个客户端存储的记录提供样本级隐私。该框架基于带Crammer-Singer损失的联邦Wasserstein分布鲁棒多分类目标构建，采用基于交替方向乘子法（ADMM）的算法求解。为保护发布的客户端更新，本文方法在中央服务器聚合前，向本地模型和本地分布鲁棒变量的近端线性化客户端更新中注入校准的高斯噪声，并通过矩会计师量化累积隐私损失。本文提供每轮样本级(ε,δ)-差分隐私保证及所有训练轮次的整体组合隐私保证，同时给出收敛结果和显式的隐私-效用权衡。在模拟工业故障数据和UCI基准数据集上的实验表明，本文方法在多数测试场景下优于差分隐私联邦经验风险最小化和随机梯度下降基线，在隐私诱导强噪声下性能提升最为显著。
英文摘要：
Fault diagnosis in asset-intensive industries requires multiclass models trained on noisy, sensitive data collected across geographically distributed client sites. Federated learning enables collaborative training without centralizing data, but most approaches do not provide formal privacy guarantees for individual records stored at client sites and can degrade under label noise. To address these limitations, we develop a differentially private, distributionally robust framework for federated multiclass classification that provides sample-level privacy for records stored at each client. We build on a federated Wasserstein distributionally robust multiclass objective with the Crammer-Singer loss, solved using an Alternating Direction Method of Multipliers (ADMM)-based algorithm. To protect the released client updates, our method injects calibrated Gaussian noise into the proximal linearized client updates of both the local model and the local distributionally robust variable before aggregation at the central server, and we quantify the cumulative privacy loss using the moments accountant. We provide per-round sample-level $(ε,δ)$-differential privacy guarantees and an overall composed privacy guarantee across all training rounds, together with convergence results and an explicit privacy-utility tradeoff. Experiments on simulated industrial fault data and UCI benchmark datasets show that our method outperforms differentially private federated empirical risk minimization and stochastic gradient descent baselines in most tested settings, with the largest gains under strong privacy-induced noise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
