论文标题
使用加法安全多方计算联合QR分解的隐私
Privacy of federated QR decomposition using additive secure multiparty computation
论文作者
论文摘要
联合学习（FL）是一种隐私感知的数据挖掘策略，将私人数据保留在所有者机器上，从而保密。客户计算本地模型，并将其发送到计算全局模型的聚合器。在Hybrid FL中，局部参数还使用安全汇总掩盖，因此只有全局汇总统计信息才能在清晰的文本中可用，而不是客户特定的更新。在跨核心联合学习的背景下，尚未对联合QR分解进行广泛的研究。在本文中，我们调查了三种QR分解算法对交叉silo fl的适用性，并提出了基于革兰氏 - schmidt算法的隐私感知QR分解方案，该方案不会公然泄漏原始数据。我们将算法应用以以联合方式计算线性回归。
Federated learning (FL) is a privacy-aware data mining strategy keeping the private data on the owners' machine and thereby confidential. The clients compute local models and send them to an aggregator which computes a global model. In hybrid FL, the local parameters are additionally masked using secure aggregation, such that only the global aggregated statistics become available in clear text, not the client specific updates. Federated QR decomposition has not been studied extensively in the context of cross-silo federated learning. In this article, we investigate the suitability of three QR decomposition algorithms for cross-silo FL and suggest a privacy-aware QR decomposition scheme based on the Gram-Schmidt algorithm which does not blatantly leak raw data. We apply the algorithm to compute linear regression in a federated manner.
