Traditional vertical federated learning schema suffers from two main issues: 1) restricted applicable scope to overlapped samples and 2) high system challenge of real-time federated serving, which limits its application to advertising systems. To this end, we advocate a new practical learning setting, Semi-VFL (Vertical Semi-Federated Learning), for real-world industrial applications, where the learned model retains sufficient advantages of federated learning while supporting independent local serving. To achieve this goal, we propose the carefully designed Joint Privileged Learning framework (JPL) to i) alleviate the absence of the passive party's feature with federated equivalence imitation and ii) adapt to the heterogeneous full sample space with cross-branch rank alignment. Extensive experiments conducted on real-world advertising datasets validate the effectiveness of our method over baseline methods.

翻译：传统垂直联邦学习范式存在两个主要问题：1) 适用范围受限于重叠样本；2) 实时联邦服务面临较高的系统挑战，这限制了其在广告系统中的应用。为此，我们提出一种面向实际工业应用的新型实用学习框架——半垂直联邦学习，该框架在保持联邦学习核心优势的同时，支持独立的本地服务。为实现这一目标，我们设计了联合特权学习框架，该框架通过以下机制实现目标：i) 利用联邦等效模拟缓解被动方特征缺失问题；ii) 通过跨分支排序对齐适应异构全样本空间。在真实广告数据集上的大量实验验证了本方法相较于基线方法的优越性。