Vertical Federated Learning

Vertical Federated Learning (VFL) is a privacy-preserving machine learning approach enabling collaborative model training across multiple parties holding different features of the same data samples, without directly sharing raw data. Current research emphasizes developing efficient algorithms and model architectures, such as split neural networks and gradient boosting decision trees, to address challenges like communication overhead, security vulnerabilities (including backdoor attacks and data reconstruction attacks), and ensuring fairness in contribution evaluation. VFL's significance lies in its potential to unlock the value of siloed datasets in various sectors (finance, healthcare, IoT) while upholding data privacy regulations, fostering trust, and facilitating collaborative data analysis.

Papers

May 7, 2024

Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference
Conor Hassan, Matthew Sutton, Antonietta Mira, Kerrie Mengersen
Data Augmentation Bayesian Inference State Estimation Vertical Federated Learning Amortized Inference Joint Inference Multilevel Model

May 3, 2024

A Survey on Contribution Evaluation in Vertical Federated Learning
Yue Cui, Chung-ju Huang, Yuzhu Zhang, Leye Wang, Lixin Fan, Xiaofang Zhou, Qiang Yang
Timely Survey Vertical Federated Learning Contribution Evaluation

May 1, 2024

PackVFL: Efficient HE Packing for Vertical Federated Learning
Liu Yang, Shuowei Cai, Di Chai, Junxue Zhang, Han Tian, Yilun Jin, Kun Guo, Kai Chen, Qiang Yang
Homomorphic Encryption Vertical Federated Learning Matrix Multiplication VFL Algorithm

April 30, 2024

Leveraging Label Information for Stealthy Data Stealing in Vertical Federated Learning
Duanyi Yao, Songze Li, Xueluan Gong, Sizai Hou, Gaoning Pan
Vertical Federated Learning Label Information Bi Discriminator Auxiliary Classifier Attack Algorithm Malicious Attack

April 27, 2024

TabVFL: Improving Latent Representation in Vertical Federated Learning
Mohamed Rashad, Zilong Zhao, Jeremie Decouchant, Lydia Y. Chen
Supervised Autoencoder Latent Representation Vertical Federated Learning Autoencoder Architecture

April 18, 2024

KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning
Marco Arazzi, Serena Nicolazzo, Antonino Nocera
Vertical Federated Learning K Content DEfense Mechanism Label Inference Attack

April 15, 2024

Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning
Chong Yu, Shuaiqi Shen, Shiqiang Wang, Kuan Zhang, Hai Zhao
Stochastic Gradient Descent Vertical Federated Learning Mobile Health Partitioned Data Vertical Pole Horizontal Federated Learning

April 6, 2024

Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning
Yan Kang, Ziyao Ren, Lixin Fan, Linghua Yang, Yongxin Tong, Qiang Yang
Hyperparameter Optimization Homomorphic Encryption Vertical Federated Learning Label Inference Attack

March 30, 2024

Computation and Communication Efficient Lightweighting Vertical Federated Learning
Heqiang Wang, Jieming Bian, Lei Wang
Computation Method Robust Learning Vertical Federated Learning Trade Offs

March 6, 2024

Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data
Avi Amalanshu, Yash Sirvi, David I. Inouye
Vertical Federated Learning Feature Learning Intermediate Representation Label Supervision Partitioned Data Training Environment Decentralized Aggregation

February 23, 2024

A Bargaining-based Approach for Feature Trading in Vertical Federated Learning
Yue Cui, Liuyi Yao, Zitao Li, Yaliang Li, Bolin Ding, Xiaofang Zhou
Vertical Federated Learning Nash Bargaining Feature Swapping

February 6, 2024

A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective
Lei Yu, Meng Han, Yiming Li, Changting Lin, Yao Zhang, Mingyang Zhang, Yan Liu, Haiqin Weng, Yuseok Jeon, Ka-Ho Chow, Stacy Patterson
Timely Survey Federated Learning Vertical Federated Learning Privacy Risk Privacy Attack Privacy Threat

February 1, 2024

Survey of Privacy Threats and Countermeasures in Federated Learning
Masahiro Hayashitani, Junki Mori, Isamu Teranishi
Timely Survey Vertical Federated Learning Empirical Privacy Rapid Countermeasure Privacy Threat

January 22, 2024

Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach
Qiqing Wang, Kaidi Yang
Privacy Preserving Vertical Federated Learning Private Data Information Fusion Privacy Preserving Federated Learning Traffic Model Traffic State Estimation

January 17, 2024

A GAN-based data poisoning framework against anomaly detection in vertical federated learning
Xiaolin Chen, Daoguang Zan, Wei Li, Bei Guan, Yongji Wang
Anomaly Detection Adversarial Perturbation C Gan Vertical Federated Learning Data Poisoning GAN Based

December 29, 2023

FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning
Jie Shen, Shusen Yang, Cong Zhao, Xuebin Ren, Peng Zhao, Yuqian Yang, Qing Han, Shuaijun Wu
Vertical Federated Learning Label Free Fault Diagnosis Federated Transfer Learning Intelligent Fault Diagnosis

December 27, 2023

Fault-Tolerant Vertical Federated Learning on Dynamic Networks
Surojit Ganguli, Zeyu Zhou, Christopher G. Brinton, David I. Inouye
LeArning Abstract Vertical Federated Learning Dynamic Network Sensor Network Online Learner

December 18, 2023

Online Vertical Federated Learning for Cooperative Spectrum Sensing
Heqiang Wang, Jie Xu
Vertical Federated Learning Centralized Learning Spectrum Sensing Spectrum Management Spectrum Utilization

December 15, 2023

A Distributed Privacy Preserving Model for the Detection of Alzheimer's Disease
Paul K. Mandal
Data Detection Multimodal Data Vertical Federated Learning Medical Data Sensitive Patient

November 22, 2023

SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning
Jian Zhang, Bowen Li Jie Li, Chentao Wu
Machine Unlearning Unlearning Framework Vertical Federated Learning Cut and Approximate Gradient Boosting Decision Tree Federated Gradient Boosting Data Deletion

Vertical Federated Learning

Papers

Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference

A Survey on Contribution Evaluation in Vertical Federated Learning

PackVFL: Efficient HE Packing for Vertical Federated Learning

Leveraging Label Information for Stealthy Data Stealing in Vertical Federated Learning

TabVFL: Improving Latent Representation in Vertical Federated Learning

KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning

Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning

Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning

Computation and Communication Efficient Lightweighting Vertical Federated Learning

Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data

A Bargaining-based Approach for Feature Trading in Vertical Federated Learning

A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective

Survey of Privacy Threats and Countermeasures in Federated Learning

Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach

A GAN-based data poisoning framework against anomaly detection in vertical federated learning

FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning

Fault-Tolerant Vertical Federated Learning on Dynamic Networks

Online Vertical Federated Learning for Cooperative Spectrum Sensing

A Distributed Privacy Preserving Model for the Detection of Alzheimer's Disease

SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning