  As an emerging secure learning paradigm in lever-aging cross-agency private
data, vertical federatedlearning (VFL) is expected to improve advertising
models by enabling the joint learning of complementary user attributes
privately owned by the advertiser and the publisher. However, there are two key
challenges in applying it to advertising systems: a) the limited scale of
labeled overlapping samples, and b) the high cost of real-time cross-agency
serving. In this paper, we propose a semi-supervised split distillation
framework VFed-SSD to alleviate the two limitations. We identify that: i)there
are massive unlabeled overlapped data available in advertising systems, and ii)
we can keep a balance between model performance and inference cost by
decomposing the federated model. Specifically, we develop a self-supervised
task MatchedPair Detection (MPD) to exploit the vertically partitioned
unlabeled data and propose the Split Knowledge Distillation (SplitKD) schema to
avoid cross-agency serving. Empirical studies on three industrial datasets
exhibit the effectiveness of ourmethods, with the median AUC over all datasets
improved by 0.86% and 2.6% in the local andthe federated deployment mode
respectively. Overall, our framework provides an efficient federation-enhanced
solution for real-time display advertising with minimal deploying cost and
significant performance lift.
