# Vertical Semi-Federated Learning for Efficient Online Advertising

arXiv: https://arxiv.org/abs/2209.15635 · HTML: https://mlsanity.com/paper/2209.15635/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 46 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 18 of 18)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [VAFL: a Method of Vertical Asynchronous Federated Learning](https://mlsanity.com/paper/2007.06081/) (2020)
- [Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings](https://mlsanity.com/paper/1904.11547/) (2019)
- [Optimized Cost per Click in Taobao Display Advertising](https://mlsanity.com/paper/1703.02091/) (2017)
- [Vertical Federated Learning: Challenges, Methodologies and Experiments](https://mlsanity.com/paper/2202.04309/) (2022)
- [Meta Matrix Factorization for Federated Rating Predictions](https://mlsanity.com/paper/1910.10086/) (2019)
- [SplitNN-driven Vertical Partitioning](https://mlsanity.com/paper/2008.04137/) (2020)
- [BlindFL: Vertical Federated Machine Learning without Peeking into Your Data](https://mlsanity.com/paper/2206.07975/) (2022)
- [FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training](https://mlsanity.com/paper/2008.10838/) (2020)
- [Label Leakage and Protection from Forward Embedding in Vertical Federated Learning](https://mlsanity.com/paper/2203.01451/) (2022)
- [Differentially Private Label Protection in Split Learning](https://mlsanity.com/paper/2203.02073/) (2022)
- [Vertical Federated Learning without Revealing Intersection Membership](https://mlsanity.com/paper/2106.05508/) (2021)
- [A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations](https://mlsanity.com/paper/2008.10808/) (2020)

## Built on by (most cited first, 12 of 12)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [Vertical Federated Learning: A Structured Literature Review](https://mlsanity.com/paper/2212.00622/) (2022)
- [Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning](https://mlsanity.com/paper/2304.10638/) (2023)
- [FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning](https://mlsanity.com/paper/2305.08328/) (2023)
- [VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks](https://mlsanity.com/paper/2307.02040/) (2023)
- [Vertical Federated Learning with Missing Features During Training and Inference](https://mlsanity.com/paper/2410.22564/) (2024)
- [On the Conflict of Robustness and Learning in Collaborative Machine Learning](https://mlsanity.com/paper/2402.13700/) (2024)
- [TabVFL: Improving Latent Representation in Vertical Federated Learning](https://mlsanity.com/paper/2404.17990/) (2024)
- [Event-Driven Online Vertical Federated Learning](https://mlsanity.com/paper/2506.14911/) (2025)
- [VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learning](https://mlsanity.com/paper/2507.14625/) (2025)
- [Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data](https://mlsanity.com/paper/2507.22488/) (2025)
- [X-VFL: A New Vertical Federated Learning Framework with Cross Completion and Decision Subspace Alignment](https://mlsanity.com/paper/2508.05568/) (2025)

## Abstract (arXiv)

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.