论文标题
VAFL：一种垂直异步联合学习的方法
VAFL: a Method of Vertical Asynchronous Federated Learning
论文作者
论文摘要
水平联合学习（FL）处理具有相同功能集的多客户数据，而垂直FL训练了一个更好的预测指标，可以结合来自不同客户端的所有功能。本文以异步方式靶向求解垂直FL，并开发出一种简单的FL方法。新方法允许每个客户端在不与其他客户协调的情况下运行随机梯度算法，因此它适用于客户的间歇连接。该方法进一步使用了一种新技术的局部嵌入技术，以确保数据隐私并提高沟通效率。从理论上讲，我们介绍了强烈凸，非凸甚至非平滑目标的方法的收敛率和隐私级别。从经验上讲，我们将我们的方法应用于各种图像和医疗保健数据集。结果与集中和同步的FL方法相比。
Horizontal Federated learning (FL) handles multi-client data that share the same set of features, and vertical FL trains a better predictor that combine all the features from different clients. This paper targets solving vertical FL in an asynchronous fashion, and develops a simple FL method. The new method allows each client to run stochastic gradient algorithms without coordination with other clients, so it is suitable for intermittent connectivity of clients. This method further uses a new technique of perturbed local embedding to ensure data privacy and improve communication efficiency. Theoretically, we present the convergence rate and privacy level of our method for strongly convex, nonconvex and even nonsmooth objectives separately. Empirically, we apply our method to FL on various image and healthcare datasets. The results compare favorably to centralized and synchronous FL methods.
