VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning
Fangcheng Fu, Yingxia Shao, Lele Yu, Jiawei Jiang, Huanran Xue, Yangyu Tao, Bin Cui
Abstract
With the ever-evolving concerns on privacy protection, vertical federated learning (FL), where participants own non-overlapping features for the same set of instances, is becoming a heated topic since it enables multiple enterprises to strengthen the machine learning models collaboratively with privacy guarantees. Nevertheless, to achieve privacy preservation, vertical FL algorithms involve complicated training routines and time-consuming cryptography operations, leading to slow training speed.
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Install the CLIlune papers get ccc2dddd-4537-4733-b75b-37f233aeab65Cited by top-tier papers15
- VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?Jiawei Jiang, Lukas Burkhalter, Fangcheng Fu, Bolin Ding et al.NeurIPS 2022 · 43 citations
- Communication-Efficient Vertical Federated Learning with Limited Overlapping SamplesJingwei Sun, Ziyue Xu, Dong Yang, Vishwesh Nath et al.ICCV 2023 · 26 citations
- A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated LearningZhaomin Wu, Qinbin Li, Bingsheng HeNeurIPS 2022 · 26 citations
- Practical Differentially Private and Byzantine-resilient Federated LearningZihang Xiang, Tianhao Wang, Wanyu Lin, Di WangSIGMOD 2023 · 22 citations
- FEAST: A Communication-efficient Federated Feature Selection Framework for Relational DataRui Fu, Yuncheng Wu, Quanqing Xu, Meihui ZhangSIGMOD 2023 · 17 citations
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