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Vertical Federated Learning

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform

Liefeng Bo, Tao Zeng, Peng Dai, Bo Liu, Chaowei Tan, Jiazhou Wang, Huasong Shan, Houpu Yao, Heng Huang, Yanqing Chen•Wed Jul 07 2021

This paper presents Fedlearn-Algo, an open-source privacy preserving machine learning platform, and releases vertical federated kernel binary classification model and vertical Federated random forest model, the first batch of novel FL algorithm examples.

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Differentially Private Vertical Federated Clustering

Tianhao Wang, Zitao Li, Ninghui Li•Mon Aug 01 2022

The algorithm proposed in this paper is the first practical solution for differentially private vertical federated k -means clustering, where the server can obtain a set of global centers with a provable differential privacy guarantee and improves the estimation accuracy in the setting with more than two data parties.

25 0

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Feature Inference Attack on Model Predictions in Vertical Federated Learning

B. Ooi, Xinjian Luo, Yuncheng Wu, Xiaokui Xiao•Mon Oct 19 2020

This paper presents several feature inference attack methods to investigate the potential privacy leakages in the model prediction stage of vertical FL and proposes two specific attacks on the logistic regression (LR) and decision tree (DT) models, according to individual prediction output.

275 0

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Interpret Federated Learning with Shapley Values

Guan Wang•Fri May 10 2019

This paper proposes a method to balance the model interpretability and data privacy in vertical Federated Learning by using Shapley values to reveal detailed feature importance for host features and a unified importance value for federated guest features.

98 0

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PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN

Daniele Romanini, A. Hall, Pavlos Papadopoulos, Tom Titcombe, Abbas Ismail, Tudor Cebere, R. Sandmann, Robin Roehm, Michael A. Hoeh•Wed Mar 31 2021

This work introduces PyVertical, a framework supporting vertical federated learning using split neural networks, and presents the training of a simple dual-headed split neural network for a MNIST classification task.

112 0

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A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning

Qinbin Li, Bingsheng He, Zhaomin Wu•Thu Jun 10 2021

A novel coupled training paradigm, FedSim, that integrates one-to-many linkage into the training process that enables VFL in many real-world applications with fuzzy identifiers and achieves better performance in traditional VFL tasks.

23 0

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Achieving Model Fairness in Vertical Federated Learning

J. Pei, Yong Zhang, Lingyang Chu, Changxin Liu, Zirui Zhou, Yang Shi•Thu Sep 16 2021

This work systematically formulate the problem of training fair models in VFL, and develops 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.

16 0

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Vertical Federated Learning | State-of-the-Art