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

October 31, 2023

Privacy-preserving design of graph neural networks with applications to vertical federated learning
Ruofan Wu, Mingyang Zhang, Lingjuan Lyu, Xiaolong Xu, Xiuquan Hao, Xinyi Fu, Tengfei Liu, Tianyi Zhang, Weiqiang Wang
Neural Network Financial Application Graph Drawing Product Design Graph Representation Learning Graph Structured Data Vertical Federated Learning GNN Training Transaction Network

October 20, 2023

VFedMH: Vertical Federated Learning for Training Multiple Heterogeneous Models
Shuo Wang, Keke Gai, Jing Yu, Liehuang Zhu, Kim-Kwang Raymond Choo, Bin Xiao
Vertical Federated Learning Heterogeneous Model Model Gradient Local Heterogeneous Model

October 15, 2023

VFLAIR: A Research Library and Benchmark for Vertical Federated Learning
Tianyuan Zou, Zixuan Gu, Yu He, Hideaki Takahashi, Yang Liu, Ya-Qin Zhang
New Benchmark Vertical Federated Learning Collaborative Training VFL Algorithm

August 18, 2023

Defending Label Inference Attacks in Split Learning under Regression Setting
Haoze Qiu, Fei Zheng, Chaochao Chen, Xiaolin Zheng
Novel Regression Vertical Federated Learning Split Learning Random Label Label Inference Attack

August 4, 2023

Label Inference Attacks against Node-level Vertical Federated GNNs
Marco Arazzi, Mauro Conti, Stefanos Koffas, Marina Krcek, Antonino Nocera, Stjepan Picek, Jing Xu
Zero Shot Vertical Federated Learning Label Inference Attack Federated Graph Neural Network

July 26, 2023

Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings
Yuxi Mi, Hongquan Liu, Yewei Xia, Yiheng Sun, Jihong Guan, Shuigeng Zhou
Differential Privacy Task Utility Vertical Federated Learning Privacy Guarantee Data Privacy Adaptive Representation

July 19, 2023

Eliminating Label Leakage in Tree-Based Vertical Federated Learning
Hideaki Takahashi, Jingjing Liu, Yang Liu
Differential Privacy Vertical Federated Learning Training Label Label Inference Attack Label Leakage

July 7, 2023

Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problem
Afsana Khan, Marijn ten Thij, Frank Thuijsman, Anna Wilbik
Machine Learning Model Vertical Federated Learning Private Data Game Theory Passive Party Financial Distress Incentive Allocation

July 5, 2023

VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks
Zhaomin Wu, Junyi Hou, Bingsheng He
Vertical Federated Learning Feature Diversity

June 28, 2023

Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimization
Ganyu Wang, Qingsong Zhang, Li Xiang, Boyu Wang, Bin Gu, Charles Ling
Vertical Federated Learning Zeroth Order Hybrid Optimization First Order Optimization

June 19, 2023

Practical and General Backdoor Attacks against Vertical Federated Learning
Yuexin Xuan, Xiaojun Chen, Zhendong Zhao, Bisheng Tang, Ye Dong
Federated Learning Backdoor Attack Vertical Federated Learning Practical Application Backdoor Trigger Attack Strategy

May 29, 2023

Reducing Communication for Split Learning by Randomized Top-k Sparsification
Fei Zheng, Chaochao Chen, Lingjuan Lyu, Binhui Yao
Timely Communication Vertical Federated Learning Split Learning Communication Cost Top K Sparsification

May 26, 2023

Secure Vertical Federated Learning Under Unreliable Connectivity
Xinchi Qiu, Heng Pan, Wanru Zhao, Yan Gao, Pedro P. B. Gusmao, William F. Shen, Chenyang Ma, Nicholas D. Lane
Homomorphic Encryption Vertical Federated Learning VFL Algorithm

May 23, 2023

To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion
Rui Li, Xu Chen, Chaozhuo Li, Yanming Shen, Jianan Zhao, Yujing Wang, Weihao Han, Hao Sun, Weiwei Deng, Qi Zhang, Xing Xie
Knowledge Graph Completion Vertical Federated Learning Entity Pair Relation Pattern

May 18, 2023

Efficient Vertical Federated Learning with Secure Aggregation
Xinchi Qiu, Heng Pan, Wanru Zhao, Chenyang Ma, Pedro Porto Buarque de Gusmão, Nicholas D. Lane
Homomorphic Encryption Vertical Federated Learning PRIvacy Leakage Secure Aggregation

May 15, 2023

May 4, 2023

Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization
Yuanming Shi, Shuhao Xia, Yong Zhou, Yijie Mao, Chunxiao Jiang, Meixia Tao
Convergence Analysis Vertical Federated Learning Air Computation Radio Access Network Fronthaul Compression System Optimization

May 3, 2023

LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning
Timothy Castiglia, Yi Zhou, Shiqiang Wang, Swanand Kadhe, Nathalie Baracaldo, Stacy Patterson
Feature Selection Vertical Federated Learning Local Feature Communication Efficient Local Datasets VFL Algorithm Feature Set

April 26, 2023

FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models
Songze Li, Duanyi Yao, Jin Liu
Vertical Federated Learning Model Privacy Secret Sharing Model Splitting VFL Algorithm Information Theoretic Privacy

Vertical Federated Learning

Papers

Privacy-preserving design of graph neural networks with applications to vertical federated learning

VFedMH: Vertical Federated Learning for Training Multiple Heterogeneous Models

VFLAIR: A Research Library and Benchmark for Vertical Federated Learning

Defending Label Inference Attacks in Split Learning under Regression Setting

Label Inference Attacks against Node-level Vertical Federated GNNs

Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings

Eliminating Label Leakage in Tree-Based Vertical Federated Learning

Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problem

VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks

Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimization

Practical and General Backdoor Attacks against Vertical Federated Learning

Reducing Communication for Split Learning by Randomized Top-k Sparsification

Secure Vertical Federated Learning Under Unreliable Connectivity

To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion

Efficient Vertical Federated Learning with Secure Aggregation

Quadratic Functional Encryption for Secure Training in Vertical Federated Learning

FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning

Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization

LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning

FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models