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Conversion rate (CVR) estimation aims to predict the probability ofconversion event after a user has clicked an ad. Typically, online publisherhas user browsing interests and click feedbacks, while demand-side advertisingplatform collects users' post-click behaviors such as dwell time and conversiondecisions. To estimate CVR accurately and protect data privacy better, verticalfederated learning (vFL) is a natural solution to combine two sides' advantagesfor training models, without exchanging raw data. Both CVR estimation andapplied vFL algorithms have attracted increasing research attentions. However,standardized and systematical evaluations are missing: due to the lack ofstandardized datasets, existing studies adopt public datasets to simulate a vFLsetting via hand-crafted feature partition, which brings challenges to faircomparison. We introduce FedAds, the first benchmark for CVR estimation withvFL, to facilitate standardized and systematical evaluations for vFLalgorithms. It contains a large-scale real world dataset collected fromAlibaba's advertising platform, as well as systematical evaluations for botheffectiveness and privacy aspects of various vFL algorithms. Besides, we alsoexplore to incorporate unaligned data in vFL to improve effectiveness, anddevelop perturbation operations to protect privacy well. We hope that futureresearch work in vFL and CVR estimation benefits from the FedAds benchmark.

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