论文标题
通过网络的大数据从大数据进行联合学习
Federated Learning From Big Data Over Networks
论文作者
论文摘要
本文制定并研究了一种从大量本地数据集收集的联合学习的新算法。该算法利用了固有网络结构，该网络结构通过无方向性的“经验”图与本地数据集相关联。我们使用网络线性回归模型通过网络对这样的大数据进行建模。每个本地数据集都有单个回归权重。本地数据集的紧密联系子收集的权重强制执行几乎没有偏差。这自然而然地适合我们使用原始偶偶方法解决的网络套索问题。我们通过该原始偶对偶的消息传递的消息获得分布式联合学习算法。我们提供了由此产生的联合学习算法的统计和计算特性的详细分析。
This paper formulates and studies a novel algorithm for federated learning from large collections of local datasets. This algorithm capitalizes on an intrinsic network structure that relates the local datasets via an undirected "empirical" graph. We model such big data over networks using a networked linear regression model. Each local dataset has individual regression weights. The weights of close-knit sub-collections of local datasets are enforced to deviate only little. This lends naturally to a network Lasso problem which we solve using a primal-dual method. We obtain a distributed federated learning algorithm via a message passing implementation of this primal-dual method. We provide a detailed analysis of the statistical and computational properties of the resulting federated learning algorithm.
