Vertical Semi-Federated Learning for Efficient Online Advertising
This addresses efficiency and scalability issues for online advertising systems, representing an incremental improvement over existing federated learning methods.
The paper tackles the limitations of traditional vertical federated learning in online advertising by proposing Semi-VFL, a new setting that supports independent local serving while retaining federated learning advantages, validated through experiments on real-world datasets.
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.