Cover Image

Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ye, Mang, Shen, Wei, Du, Bo, Snezhko, Eduard, Kovalev, Vassili, Yuen, Pong C.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FedThief: Harming Others to Benefit Oneself in Self-Centered Federated Learning
by: Zhang, Xiangyu, et al.
Published: (2025)

Secure Vertical Federated Learning Under Unreliable Connectivity
by: Qiu, Xinchi, et al.
Published: (2023)

FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
by: Tan, Zihan, et al.
Published: (2024)

A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Example
by: Wang, Huan-Chih, et al.
Published: (2024)

Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
by: Xie, Chulin, et al.
Published: (2022)

Decentralized Federated Learning: A Survey on Security and Privacy
by: Hallaji, Ehsan, et al.
Published: (2024)

Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit
by: Yao, Duanyi, et al.
Published: (2024)

Federated Learning with Only Positive Labels by Exploring Label Correlations
by: An, Xuming, et al.
Published: (2024)

Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning
by: Lee, Seohyun, et al.
Published: (2025)

Preserving Privacy and Security in Federated Learning
by: Nguyen, Truc, et al.
Published: (2022)

PackVFL: Efficient HE Packing for Vertical Federated Learning
by: Yang, Liu, et al.
Published: (2024)

URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning
by: Yao, Duanyi, et al.
Published: (2024)

Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning
by: Jiang, Yu, et al.
Published: (2025)

FedRE: Robust and Effective Federated Learning with Privacy Preference
by: Xiao, Tianzhe, et al.
Published: (2025)

FedRecAttack: Model Poisoning Attack to Federated Recommendation
by: Rong, Dazhong, et al.
Published: (2022)

Is the Trigger Essential? A Feature-Based Triggerless Backdoor Attack in Vertical Federated Learning
by: Liu, Yige, et al.
Published: (2026)

KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning
by: Arazzi, Marco, et al.
Published: (2024)

VFLGAN-TS: Vertical Federated Learning-based Generative Adversarial Networks for Publication of Vertically Partitioned Time-Series Data
by: Yuan, Xun, et al.
Published: (2024)

HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning
by: Qiu, Pengyu, et al.
Published: (2022)

Survey of Privacy Threats and Countermeasures in Federated Learning
by: Hayashitani, Masahiro, et al.
Published: (2024)

LabObf: A Label Protection Scheme for Vertical Federated Learning Through Label Obfuscation
by: He, Ying, et al.
Published: (2024)

DDP-SA: Scalable Privacy-Preserving Federated Learning via Distributed Differential Privacy and Secure Aggregation
by: Wei, Wenjing, et al.
Published: (2026)

A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective
by: Yu, Lei, et al.
Published: (2024)

Secure UAV-assisted Federated Learning: A Digital Twin-Driven Approach with Zero-Knowledge Proofs
by: Zami, Md Bokhtiar Al, et al.
Published: (2025)

SLVR: Securely Leveraging Client Validation for Robust Federated Learning
by: Choi, Jihye, et al.
Published: (2025)

Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning
by: Zhang, Yiwei, et al.
Published: (2024)

SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks
by: Tavallaie, Omid, et al.
Published: (2024)

FuSeFL: Fully Secure and Scalable Federated Learning
by: Ghinani, Sahar Ghoflsaz, et al.
Published: (2025)

Revisiting Label Inference Attacks in Vertical Federated Learning: Why They Are Vulnerable and How to Defend
by: Liu, Yige, et al.
Published: (2026)

A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security
by: Ceviz, Ozlem, et al.
Published: (2023)

FBChain: A Blockchain-based Federated Learning Model with Efficiency and Secure Communication
by: Li, Yang, et al.
Published: (2023)

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution
by: Wang, Jinbo, et al.
Published: (2024)

A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises
by: Fotohi, Reza, et al.
Published: (2025)

Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning
by: Yang, Xue, et al.
Published: (2024)

Securing Federated Learning against Backdoor Threats with Foundation Model Integration
by: Bi, Xiaohuan, et al.
Published: (2024)

PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework
by: Commey, Daniel, et al.
Published: (2025)

Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration
by: Zhang, Yiwei, et al.
Published: (2025)

Secure and Privacy-Preserving Federated Learning for Next-Generation Underground Mine Safety
by: Elmahallawy, Mohamed, et al.
Published: (2025)

On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps
by: Heikkilä, Mikko A.
Published: (2024)

Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation
by: Yang, Kun, et al.
Published: (2025)