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  3. 2009.03561
  4. Cited By
Local and Central Differential Privacy for Robustness and Privacy in
  Federated Learning

Local and Central Differential Privacy for Robustness and Privacy in Federated Learning

8 September 2020
Mohammad Naseri
Jamie Hayes
Emiliano De Cristofaro
    FedML
ArXivPDFHTML

Papers citing "Local and Central Differential Privacy for Robustness and Privacy in Federated Learning"

26 / 26 papers shown
Title
Decoding FL Defenses: Systemization, Pitfalls, and Remedies
Decoding FL Defenses: Systemization, Pitfalls, and Remedies
M. A. Khan
Virat Shejwalkar
Yasra Chandio
Amir Houmansadr
Fatima M. Anwar
AAML
38
0
0
03 Feb 2025
Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning
Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning
Bin Li
Xiaoye Miao
Yongheng Shang
Xinkui Zhao
AAML
49
0
0
08 Jan 2025
Universally Harmonizing Differential Privacy Mechanisms for Federated
  Learning: Boosting Accuracy and Convergence
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence
Shuya Feng
Meisam Mohammady
Hanbin Hong
Shenao Yan
Ashish Kundu
Binghui Wang
Yuan Hong
FedML
44
3
0
20 Jul 2024
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning
Shihua Sun
Shridatt Sugrim
Angelos Stavrou
Haining Wang
AAML
63
1
0
13 Jul 2024
A Systematic Review of Federated Generative Models
A Systematic Review of Federated Generative Models
Ashkan Vedadi Gargary
Emiliano De Cristofaro
AI4CE
38
2
0
26 May 2024
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey
Chaoyu Zhang
Shaoyu Li
AILaw
52
3
0
25 Feb 2024
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
Yuecheng Li
Lele Fu
Tong Wang
Jian Lou
Bin Chen
Lei Yang
Zibin Zheng
Zibin Zheng
Chuan Chen
FedML
70
4
0
10 Feb 2024
Federated learning with differential privacy and an untrusted aggregator
Federated learning with differential privacy and an untrusted aggregator
Kunlong Liu
Trinabh Gupta
50
0
0
17 Dec 2023
Avoid Adversarial Adaption in Federated Learning by Multi-Metric
  Investigations
Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations
T. Krauß
Alexandra Dmitrienko
AAML
27
4
0
06 Jun 2023
BadVFL: Backdoor Attacks in Vertical Federated Learning
BadVFL: Backdoor Attacks in Vertical Federated Learning
Mohammad Naseri
Yufei Han
Emiliano De Cristofaro
FedML
AAML
29
11
0
18 Apr 2023
Differentially Private Distributed Convex Optimization
Differentially Private Distributed Convex Optimization
Minseok Ryu
Kibaek Kim
FedML
27
1
0
28 Feb 2023
FederatedTrust: A Solution for Trustworthy Federated Learning
FederatedTrust: A Solution for Trustworthy Federated Learning
Pedro Miguel Sánchez Sánchez
Alberto Huertas Celdrán
Ning Xie
Gérome Bovet
Gregorio Martínez Pérez
Burkhard Stiller
36
21
0
20 Feb 2023
BayBFed: Bayesian Backdoor Defense for Federated Learning
BayBFed: Bayesian Backdoor Defense for Federated Learning
Kavita Kumari
Phillip Rieger
Hossein Fereidooni
Murtuza Jadliwala
A. Sadeghi
AAML
FedML
31
33
0
23 Jan 2023
Mixed Precision Quantization to Tackle Gradient Leakage Attacks in
  Federated Learning
Mixed Precision Quantization to Tackle Gradient Leakage Attacks in Federated Learning
Pretom Roy Ovi
Emon Dey
Nirmalya Roy
A. Gangopadhyay
FedML
26
4
0
22 Oct 2022
Unraveling the Connections between Privacy and Certified Robustness in
  Federated Learning Against Poisoning Attacks
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
Chulin Xie
Yunhui Long
Pin-Yu Chen
Qinbin Li
Arash Nourian
Sanmi Koyejo
Bo Li
FedML
43
13
0
08 Sep 2022
Cerberus: Exploring Federated Prediction of Security Events
Cerberus: Exploring Federated Prediction of Security Events
Mohammad Naseri
Yufei Han
Enrico Mariconti
Yun Shen
Gianluca Stringhini
Emiliano De Cristofaro
FedML
45
14
0
07 Sep 2022
Joint Privacy Enhancement and Quantization in Federated Learning
Joint Privacy Enhancement and Quantization in Federated Learning
Natalie Lang
Elad Sofer
Tomer Shaked
Nir Shlezinger
FedML
37
46
0
23 Aug 2022
PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning
PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning
Yacine Belal
A. Bellet
Sonia Ben Mokhtar
Vlad Nitu
21
10
0
09 Aug 2022
Enhanced Security and Privacy via Fragmented Federated Learning
Enhanced Security and Privacy via Fragmented Federated Learning
N. Jebreel
J. Domingo-Ferrer
Alberto Blanco-Justicia
David Sánchez
FedML
33
26
0
13 Jul 2022
Fine-grained Poisoning Attack to Local Differential Privacy Protocols
  for Mean and Variance Estimation
Fine-grained Poisoning Attack to Local Differential Privacy Protocols for Mean and Variance Estimation
Xiaoguang Li
Ninghui Li
Wenhai Sun
Neil Zhenqiang Gong
Hui Li
AAML
66
15
0
24 May 2022
Securing Federated Sensitive Topic Classification against Poisoning
  Attacks
Securing Federated Sensitive Topic Classification against Poisoning Attacks
Tianyue Chu
Álvaro García-Recuero
Costas Iordanou
Georgios Smaragdakis
Nikolaos Laoutaris
41
9
0
31 Jan 2022
Scatterbrained: A flexible and expandable pattern for decentralized
  machine learning
Scatterbrained: A flexible and expandable pattern for decentralized machine learning
Miller Wilt
Jordan K Matelsky
A. Gearhart
FedML
OOD
24
4
0
14 Dec 2021
A Distributed Privacy-Preserving Learning Dynamics in General Social
  Networks
A Distributed Privacy-Preserving Learning Dynamics in General Social Networks
Youming Tao
Shuzhen Chen
Feng Li
Dongxiao Yu
Jiguo Yu
Hao Sheng
FedML
19
3
0
15 Nov 2020
Backdooring and Poisoning Neural Networks with Image-Scaling Attacks
Backdooring and Poisoning Neural Networks with Image-Scaling Attacks
Erwin Quiring
Konrad Rieck
AAML
54
70
0
19 Mar 2020
Threats to Federated Learning: A Survey
Threats to Federated Learning: A Survey
Lingjuan Lyu
Han Yu
Qiang Yang
FedML
202
434
0
04 Mar 2020
Analyzing Federated Learning through an Adversarial Lens
Analyzing Federated Learning through an Adversarial Lens
A. Bhagoji
Supriyo Chakraborty
Prateek Mittal
S. Calo
FedML
191
1,032
0
29 Nov 2018
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