Cover Image

Building Gradient Bridges: Label Leakage from Restricted Gradient Sharing in Federated Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Rui, Chow, Ka-Ho, Li, Ping
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

Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning
by: Shan, Junjie, et al.
Published: (2024)

Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
by: Fan, Mingyuan, et al.
Published: (2022)

On the Efficiency of Privacy Attacks in Federated Learning
by: Tabassum, Nawrin, et al.
Published: (2024)

Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning
by: Kim, Joon, et al.
Published: (2024)

Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering
by: Rashid, Md Rafi Ur, et al.
Published: (2023)

Understanding Deep Gradient Leakage via Inversion Influence Functions
by: Zhang, Haobo, et al.
Published: (2023)

Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning
by: Wang, Fei, et al.
Published: (2025)

FedGIG: Graph Inversion from Gradient in Federated Learning
by: Xiao, Tianzhe, et al.
Published: (2024)

DictPFL: Efficient and Private Federated Learning on Encrypted Gradients
by: Xue, Jiaqi, et al.
Published: (2025)

A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
by: Shao, Jiawei, et al.
Published: (2023)

Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control
by: Chow, Ka-Ho, et al.
Published: (2024)

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage
by: Meng, Jiayang, et al.
Published: (2025)

Confundo: Learning to Generate Robust Poison for Practical RAG Systems
by: Hu, Haoyang, et al.
Published: (2026)

A New Federated Learning Framework Against Gradient Inversion Attacks
by: Guo, Pengxin, et al.
Published: (2024)

On the Adversarial Robustness of Graph Neural Networks with Graph Reduction
by: Wu, Kerui, et al.
Published: (2024)

ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery
by: Gong, Zirui, et al.
Published: (2026)

FedGA: Federated Learning with Gradient Alignment for Error Asymmetry Mitigation
by: Xiao, Chenguang, et al.
Published: (2024)

Location Leakage in Federated Signal Maps
by: Bakopoulou, Evita, et al.
Published: (2021)

Approaching the Harm of Gradient Attacks While Only Flipping Labels
by: El-Kabid, Abdessamad, et al.
Published: (2025)

Harmless Backdoor-based Client-side Watermarking in Federated Learning
by: Luo, Kaijing, et al.
Published: (2024)

Privacy Leakage via Output Label Space and Differentially Private Continual Learning
by: Tobaben, Marlon, et al.
Published: (2024)

Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning
by: Wang, Zhibo, et al.
Published: (2024)

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

Whispers of Data: Unveiling Label Distributions in Federated Learning Through Virtual Client Simulation
by: Ma, Zhixuan, et al.
Published: (2025)

Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping
by: Pelikan, Martin, et al.
Published: (2023)

Practical Feasibility of Gradient Inversion Attacks in Federated Learning
by: Valadi, Viktor, et al.
Published: (2025)

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

Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks
by: Xue, Lulu, et al.
Published: (2024)

Analysis of Privacy Leakage in Federated Large Language Models
by: Vu, Minh N., et al.
Published: (2024)

Recovering Labels from Local Updates in Federated Learning
by: Chen, Huancheng, et al.
Published: (2024)

DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning
by: Liu, Yixuan, et al.
Published: (2024)

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

On the Robustness of Graph Reduction Against GNN Backdoor
by: Zhu, Yuxuan, et al.
Published: (2024)

Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation
by: Shi, Haonan, et al.
Published: (2025)

SoK: On Gradient Leakage in Federated Learning
by: Du, Jiacheng, et al.
Published: (2024)

No More Guessing: a Verifiable Gradient Inversion Attack in Federated Learning
by: Diana, Francesco, et al.
Published: (2026)

An Efficient Gradient-Based Inference Attack for Federated Learning
by: Montaña-Fernández, Pablo, et al.
Published: (2025)

Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering
by: Zhu, Rui, et al.
Published: (2023)

Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformation
by: Jiang, Yukun, et al.
Published: (2024)

zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning
by: Wang, Zhipeng, et al.
Published: (2023)