Papers
Communities
Events
Blog
Pricing
Search
Open menu
Home
Papers
2206.04055
Cited By
Gradient Obfuscation Gives a False Sense of Security in Federated Learning
8 June 2022
Kai Yue
Richeng Jin
Chau-Wai Wong
D. Baron
H. Dai
FedML
Re-assign community
ArXiv
PDF
HTML
Papers citing
"Gradient Obfuscation Gives a False Sense of Security in Federated Learning"
36 / 36 papers shown
Title
Securing Genomic Data Against Inference Attacks in Federated Learning Environments
Chetan Pathade
Shubham Patil
21
0
0
12 May 2025
Empirical Calibration and Metric Differential Privacy in Language Models
Pedro Faustini
Natasha Fernandes
Annabelle McIver
Mark Dras
60
0
0
18 Mar 2025
FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning
Mingcong Xu
Xiaojin Zhang
Wei Chen
Hai Jin
FedML
38
0
0
08 Mar 2025
NET-SA: An Efficient Secure Aggregation Architecture Based on In-Network Computing
Qingqing Ren
Wen Wang
Shuyong Zhu
Zhiyuan Wu
Yujun Zhang
33
0
0
02 Jan 2025
Federated Learning Nodes Can Reconstruct Peers' Image Data
Ethan Wilson
Kai Yue
Chau-Wai Wong
H. Dai
FedML
12
1
0
07 Oct 2024
Analyzing Inference Privacy Risks Through Gradients in Machine Learning
Zhuohang Li
Andrew Lowy
Jing Liu
T. Koike-Akino
K. Parsons
Bradley Malin
Ye Wang
FedML
23
1
0
29 Aug 2024
Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review
Adel ElZemity
Budi Arief
21
1
0
25 Jul 2024
Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense
Wenjie Li
K. Fan
Jingyuan Zhang
Hui Li
Wei Yang Bryan Lim
Qiang Yang
AAML
FedML
25
0
0
29 May 2024
Leakage-Resilient and Carbon-Neutral Aggregation Featuring the Federated AI-enabled Critical Infrastructure
Zehang Deng
Ruoxi Sun
Minhui Xue
Sheng Wen
S. Çamtepe
Surya Nepal
Yang Xiang
35
1
0
24 May 2024
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy
Yichuan Shi
Olivera Kotevska
Viktor Reshniak
Abhishek Singh
Ramesh Raskar
AAML
22
1
0
16 May 2024
Secure Aggregation Meets Sparsification in Decentralized Learning
Sayan Biswas
Anne-Marie Kermarrec
Rafael Pires
Rishi Sharma
Milos Vujasinovic
21
0
0
13 May 2024
Beyond Noise: Privacy-Preserving Decentralized Learning with Virtual Nodes
Sayan Biswas
Mathieu Even
Anne-Marie Kermarrec
Laurent Massoulie
Rafael Pires
Rishi Sharma
M. Vos
36
3
0
15 Apr 2024
Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning
Hongsheng Hu
Shuo Wang
Tian Dong
Minhui Xue
AAML
16
17
0
04 Apr 2024
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
Yichang Xu
Ming Yin
Minghong Fang
Neil Zhenqiang Gong
OOD
FedML
21
6
0
05 Mar 2024
Edge Detectors Can Make Deep Convolutional Neural Networks More Robust
Jin Ding
Jie-Chao Zhao
Yong-zhi Sun
Ping Tan
Jia-Wei Wang
Ji-en Ma
You-tong Fang
AAML
31
2
0
26 Feb 2024
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data
Richeng Jin
Yujie Gu
Kai Yue
Xiaofan He
Zhaoyang Zhang
Huaiyu Dai
FedML
13
0
0
16 Feb 2024
Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance
Wenqi Wei
Ling Liu
13
16
0
02 Feb 2024
Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning
Zhifeng Jiang
Peng Ye
Shiqi He
Wei Wang
Ruichuan Chen
Bo Li
11
2
0
05 Jan 2024
A Comprehensive Survey of Attack Techniques, Implementation, and Mitigation Strategies in Large Language Models
Aysan Esmradi
Daniel Wankit Yip
C. Chan
AAML
25
11
0
18 Dec 2023
Improving the Robustness of Transformer-based Large Language Models with Dynamic Attention
Lujia Shen
Yuwen Pu
Shouling Ji
Changjiang Li
Xuhong Zhang
Chunpeng Ge
Ting Wang
AAML
19
3
0
29 Nov 2023
PriPrune: Quantifying and Preserving Privacy in Pruned Federated Learning
Tianyue Chu
Mengwei Yang
Nikolaos Laoutaris
A. Markopoulou
14
4
0
30 Oct 2023
PA-iMFL: Communication-Efficient Privacy Amplification Method against Data Reconstruction Attack in Improved Multi-Layer Federated Learning
Jianhua Wang
Xiaolin Chang
Jelena Mivsić
Vojislav B. Mivsić
Zhi Chen
Junchao Fan
34
2
0
25 Sep 2023
A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services
Hongsheng Hu
Shuo Wang
Jiamin Chang
Haonan Zhong
Ruoxi Sun
Shuang Hao
Haojin Zhu
Minhui Xue
MU
19
25
0
15 Sep 2023
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning
Kostadin Garov
Dimitar I. Dimitrov
Nikola Jovanović
Martin Vechev
AAML
FedML
16
7
0
05 Jun 2023
A Privacy Preserving System for Movie Recommendations Using Federated Learning
David Neumann
Andreas Lutz
Karsten Müller
Wojciech Samek
11
9
0
07 Mar 2023
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy
Yifei Zhang
Dun Zeng
Jinglong Luo
Zenglin Xu
Irwin King
FedML
69
47
0
21 Feb 2023
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
Mingyuan Fan
Cen Chen
Chengyu Wang
Ximeng Liu
Wenmeng Zhou
Jun Huang
AAML
FedML
19
0
0
05 Dec 2022
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification
Yuxin Wen
Jonas Geiping
Liam H. Fowl
Micah Goldblum
Tom Goldstein
FedML
74
91
0
01 Feb 2022
Location Leakage in Federated Signal Maps
Evita Bakopoulou
Justin Ley
Jiang Zhang
Konstantinos Psounis
A. Markopoulou
FedML
11
5
0
07 Dec 2021
Federated Deep Learning with Bayesian Privacy
Hanlin Gu
Lixin Fan
Bowen Li Jie Li
Yan Kang
Yuan Yao
Qiang Yang
FedML
75
24
0
27 Sep 2021
MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers
A. Boutet
Thomas LeBrun
Jan Aalmoes
Adrien Baud
FedML
43
17
0
26 Sep 2021
A Field Guide to Federated Optimization
Jianyu Wang
Zachary B. Charles
Zheng Xu
Gauri Joshi
H. B. McMahan
...
Mi Zhang
Tong Zhang
Chunxiang Zheng
Chen Zhu
Wennan Zhu
FedML
173
410
0
14 Jul 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
Da Yu
Huishuai Zhang
Wei Chen
Tie-Yan Liu
FedML
SILM
91
110
0
25 Feb 2021
Threats to Federated Learning: A Survey
Lingjuan Lyu
Han Yu
Qiang Yang
FedML
186
432
0
04 Mar 2020
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
Amirhossein Reisizadeh
Aryan Mokhtari
Hamed Hassani
Ali Jadbabaie
Ramtin Pedarsani
FedML
157
758
0
28 Sep 2019
Image-to-Image Translation with Conditional Adversarial Networks
Phillip Isola
Jun-Yan Zhu
Tinghui Zhou
Alexei A. Efros
SSeg
212
19,387
0
21 Nov 2016
1