This paper presents a novel approach to classical linear regression, enabling model computation from data streams or in a distributed setting while preserving data privacy in federated environments. We extend this framework to generalized linear models (GLMs), ensuring scalability and adaptability to diverse data distributions while maintaining privacy-preserving properties. To assess the effectiveness of our approach, we conduct numerical studies on both simulated and real datasets, comparing our method with conventional maximum likelihood estimation for GLMs using iteratively reweighted least squares. Our results demonstrate the advantages of the proposed method in distributed and federated settings.

翻译：本文提出了一种针对经典线性回归的新颖方法，能够在联邦环境中从数据流或分布式设置中进行模型计算，同时保护数据隐私。我们将该框架扩展到广义线性模型（GLMs），在保持隐私保护特性的同时，确保其可扩展性以及对多样化数据分布的适应性。为评估所提方法的有效性，我们在模拟和真实数据集上进行了数值研究，将我们的方法与使用迭代重加权最小二乘法的传统GLM最大似然估计进行了比较。结果表明，所提方法在分布式和联邦设置中具有显著优势。