FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning.

@inproceedings{wei-2023-fedads, author = {Penghui Wei and Hongjian Dou and Shaoguo Liu and Rongjun Tang and Li Liu and Liang Wang and Bo Zheng}, editor = {Hsin-Hsi Chen and Wei-Jou (Edward) Duh and Hen-Hsen Huang and Makoto P. Kato and Josiane Mothe and Barbara Poblete}, title = {{FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning}}, booktitle = {{Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023}}, pages = {3037--3046}, publisher = {ACM}, year = {2023}, url = {https://doi.org/10.1145/3539618.3591909}, doi = {https://doi.org/10.1145/3539618.3591909} }