Vertical Federated Learning
41 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Vertical Federated Learning
Most implemented papers
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
We use this platform to demonstrate our research and development results on privacy preserving machine learning algorithms.
Differentially Private Vertical Federated Clustering
To enable model learning while protecting the privacy of the data subjects, we need vertical federated learning (VFL) techniques, where the data parties share only information for training the model, instead of the private data.
Label Inference Attacks Against Vertical Federated Learning
FuChong-cyber/label-inference-attacks
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USENIX Security 22 2022
However, we discover that the bottom model structure and the gradient update mechanism of VFL can be exploited by a malicious participant to gain the power to infer the privately owned labels.
Federated Machine Learning: Concept and Applications
We propose a possible solution to these challenges: secure federated learning.
Interpret Federated Learning with Shapley Values
For host party to interpret a single prediction of vertical Federated Learning model, the interpretation results, namely the feature importance, are very likely to reveal the protected data from guest party.
FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training
yankang18/FedCVT
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In this article, we propose Federated Cross-view Training (FedCVT), a semi-supervised learning approach that improves the performance of the VFL model with limited aligned samples.
Feature Inference Attack on Model Predictions in Vertical Federated Learning
xj231/featureinference-vfl
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Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data collaboration without revealing their private data to each other.
PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN
We introduce PyVertical, a framework supporting vertical federated learning using split neural networks.
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning
However, most existing studies in VFL disregard the "record linkage" process.
Achieving Model Fairness in Vertical Federated Learning
To solve it in a federated and privacy-preserving manner, we consider the equivalent dual form of the problem and develop an asynchronous gradient coordinate-descent ascent algorithm, where some active data parties perform multiple parallelized local updates per communication round to effectively reduce the number of communication rounds.
