Vertical Federated Learning
26 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Vertical Federated Learning
Most implemented papers
SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning
FederatedAI/FATE
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Gradient boosting decision tree (GBDT) is a widely used ensemble algorithm in the industry.
CAFE: Catastrophic Data Leakage in Vertical Federated Learning
derafael/cafe
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We name our proposed method as catastrophic data leakage in vertical federated learning (CAFE).
Catastrophic Data Leakage in Vertical Federated Learning
derafael/cafe
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NeurIPS 2021
We name our proposed method as catastrophic data leakage in vertical federated learning (CAFE).
ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems
Vertical federated learning (VFL) system has recently become prominent as a concept to process data distributed across many individual sources without the need to centralize it.
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning
In order to learn a fair unified representation, we send it to each platform storing fairness-sensitive features and apply adversarial learning to remove bias from the unified representation inherited from the biased data.
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
To address these challenges, in this paper, we introduce a VFL framework with multiple heads (VIM), which takes the separate contribution of each client into account, and enables an efficient decomposition of the VFL optimization objective to sub-objectives that can be iteratively tackled by the server and the clients on their own.
Practical Vertical Federated Learning with Unsupervised Representation Learning
As societal concerns on data privacy recently increase, we have witnessed data silos among multiple parties in various applications.
A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning
In this work, we propose a Federated Hybrid Self-Supervised Learning framework, named FedHSSL, that utilizes cross-party views (i. e., dispersed features) of samples aligned among parties and local views (i. e., augmentation) of unaligned samples within each party to improve the representation learning capability of the VFL joint model.
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization
Although the solution based on Local Differential Privacy (LDP) addresses the above problems, it leads to the low accuracy of the trained model.
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering
Vertical federated learning (VFL), where data features are stored in multiple parties distributively, is an important area in machine learning.
