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When the Curious Abandon Honesty: Federated Learning Is Not Private
v1v2 (latest)

When the Curious Abandon Honesty: Federated Learning Is Not Private

European Symposium on Security and Privacy (EuroS&P), 2021
6 December 2021
Franziska Boenisch
Adam Dziedzic
R. Schuster
Ali Shahin Shamsabadi
Ilia Shumailov
Nicolas Papernot
    FedMLAAML
ArXiv (abs)PDFHTML

Papers citing "When the Curious Abandon Honesty: Federated Learning Is Not Private"

50 / 119 papers shown
Privacy-Utility-Bias Trade-offs for Privacy-Preserving Recommender Systems
Privacy-Utility-Bias Trade-offs for Privacy-Preserving Recommender Systems
Shiva Parsarad
Isabel Wagner
94
0
0
27 Nov 2025
SPEAR++: Scaling Gradient Inversion via Sparsely-Used Dictionary Learning
SPEAR++: Scaling Gradient Inversion via Sparsely-Used Dictionary Learning
Alexander Bakarsky
Dimitar I. Dimitrov
Maximilian Baader
Martin Vechev
FedML
96
0
0
28 Oct 2025
The Role of Federated Learning in Improving Financial Security: A Survey
The Role of Federated Learning in Improving Financial Security: A Survey
Cade Houston Kennedy
Amr Hilal
Morteza Momeni
AIFin
136
0
0
07 Oct 2025
MAUI: Reconstructing Private Client Data in Federated Transfer Learning
MAUI: Reconstructing Private Client Data in Federated Transfer Learning
Ahaan Dabholkar
Atul Sharma
Z. Berkay Celik
S. Bagchi
147
0
0
14 Sep 2025
Verifiability and Privacy in Federated Learning through Context-Hiding Multi-Key Homomorphic Authenticators
Verifiability and Privacy in Federated Learning through Context-Hiding Multi-Key Homomorphic Authenticators
Simone Bottoni
Giulio Zizzo
S. Braghin
Alberto Trombetta
AAMLFedML
197
0
0
05 Sep 2025
FedThief: Harming Others to Benefit Oneself in Self-Centered Federated Learning
FedThief: Harming Others to Benefit Oneself in Self-Centered Federated Learning
Xiangyu Zhang
Mang Ye
FedML
239
0
0
30 Aug 2025
On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions
On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions
Daniel Gutiérrez
Yelizaveta Falkouskaya
Jose L. Hernandez-Ramos
Aris Anagnostopoulos
I. Chatzigiannakis
A. Vitaletti
FedML
152
2
0
19 Aug 2025
SoK: Data Minimization in Machine Learning
SoK: Data Minimization in Machine Learning
Robin Staab
Nikola Jovanović
Kimberly Mai
Prakhar Ganesh
Martin Vechev
Ferdinando Fioretto
Matthew Jagielski
153
0
0
14 Aug 2025
A Comprehensive Review of Datasets for Clinical Mental Health AI Systems
A Comprehensive Review of Datasets for Clinical Mental Health AI Systems
Aishik Mandal
Prottay Kumar Adhikary
Hiba Arnaout
Iryna Gurevych
Tanmoy Chakraborty
AI4MH
133
0
0
13 Aug 2025
Per-element Secure Aggregation against Data Reconstruction Attacks in Federated Learning
Per-element Secure Aggregation against Data Reconstruction Attacks in Federated Learning
Takumi Suimon
Yuki Koizumi
Junji Takemasa
Toru Hasegawa
FedML
191
0
0
06 Aug 2025
Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates
Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates
Kai Yue
Richeng Jin
Chau-Wai Wong
H. Dai
AAML
247
0
0
13 Jun 2025
Gradient Inversion Attacks on Parameter-Efficient Fine-TuningComputer Vision and Pattern Recognition (CVPR), 2025
Hasin Us Sami
Swapneel Sen
Amit K. Roy-Chowdhury
S. Krishnamurthy
Başak Güler
FedMLSILM
258
3
0
04 Jun 2025
DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems
DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems
Hithem Lamri
Manaar Alam
Haiyan Jiang
Michail Maniatakos
MU
165
0
0
02 Jun 2025
Covert Attacks on Machine Learning Training in Passively Secure MPC
Covert Attacks on Machine Learning Training in Passively Secure MPCIACR Cryptology ePrint Archive (IACR ePrint), 2025
Matthew Jagielski
Daniel Escudero
Rahul Rachuri
Peter Scholl
305
0
0
21 May 2025
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated LearningConference on Uncertainty in Artificial Intelligence (UAI), 2025
Francesco Diana
André Nusser
Chuan Xu
Giovanni Neglia
389
0
0
15 May 2025
Securing Genomic Data Against Inference Attacks in Federated Learning Environments
Securing Genomic Data Against Inference Attacks in Federated Learning Environments
Chetan Pathade
Shubham Patil
236
2
0
12 May 2025
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges
Qiongxiu Li
Wenrui Yu
Yufei Xia
Jun Pang
FedML
234
6
0
10 Mar 2025
FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning
Mingcong Xu
Xiaojin Zhang
Wei Chen
Hai Jin
FedML
208
0
0
08 Mar 2025
Controlled privacy leakage propagation throughout overlapping grouped learningIEEE Journal on Selected Areas in Information Theory (JSAIT), 2025
Shahrzad Kiani
Franziska Boenisch
S. Draper
FedML
283
0
0
06 Mar 2025
GRAIN: Exact Graph Reconstruction from GradientsInternational Conference on Learning Representations (ICLR), 2025
Maria Drencheva
Ivo Petrov
Maximilian Baader
Dimitar I. Dimitrov
Martin Vechev
FedML
327
3
0
03 Mar 2025
Differentially Private Federated Learning With Time-Adaptive Privacy Spending
Differentially Private Federated Learning With Time-Adaptive Privacy SpendingInternational Conference on Learning Representations (ICLR), 2025
Shahrzad Kiani
Nupur Kulkarni
Adam Dziedzic
S. Draper
Franziska Boenisch
FedML
546
5
0
25 Feb 2025
Smoothed Normalization for Efficient Distributed Private Optimization
Smoothed Normalization for Efficient Distributed Private Optimization
Egor Shulgin
Sarit Khirirat
Peter Richtárik
FedML
395
1
0
20 Feb 2025
Privacy-Preserving Dataset Combination
Privacy-Preserving Dataset Combination
Keren Fuentes
Mimee Xu
Irene Chen
357
0
0
09 Feb 2025
Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks
Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial AttacksIEEE Conference on Technologies for Sustainability (TS), 2025
Yohannis Kifle Telila
Damitha Senevirathne
Dumindu Tissera
Apurva Narayan
Miriam A.M. Capretz
Katarina Grolinger
AAML
145
2
0
07 Feb 2025
From Counterfactuals to Trees: Competitive Analysis of Model Extraction Attacks
From Counterfactuals to Trees: Competitive Analysis of Model Extraction Attacks
Awa Khouna
Julien Ferry
Thibaut Vidal
AAML
287
0
0
07 Feb 2025
Gradient Inversion Attack on Graph Neural Networks
Gradient Inversion Attack on Graph Neural Networks
Divya Anand Sinha
Ruijie Du
Yezi Liu
Athina Markopolou
Yanning Shen
FedML
337
3
0
29 Nov 2024
Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep
  Learning
Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning
Yiwei Zhang
R. Behnia
A. Yavuz
Reza Ebrahimi
E. Bertino
FedML
226
6
0
13 Oct 2024
Federated Learning in Practice: Reflections and Projections
Federated Learning in Practice: Reflections and ProjectionsInternational Conference on Trust, Privacy and Security in Intelligent Systems and Applications (ICPSISA), 2024
Katharine Daly
Hubert Eichner
Peter Kairouz
H. B. McMahan
Daniel Ramage
Zheng Xu
FedML
316
29
0
11 Oct 2024
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
Kristian Schwethelm
Johannes Kaiser
Jonas Kuntzer
Mehmet Yigitsoy
Daniel Rueckert
Georgios Kaissis
318
0
0
01 Oct 2024
Privacy Attack in Federated Learning is Not Easy: An Experimental Study
Privacy Attack in Federated Learning is Not Easy: An Experimental Study
Hangyu Zhu
Liyuan Huang
Zhenping Xie
FedML
257
2
0
28 Sep 2024
UTrace: Poisoning Forensics for Private Collaborative Learning
UTrace: Poisoning Forensics for Private Collaborative Learning
Evan Rose
Hidde Lycklama
Harsh Chaudhari
Niklas Britz
Anwar Hithnawi
Alina Oprea
465
2
0
23 Sep 2024
Re-Evaluating Privacy in Centralized and Decentralized Learning: An
  Information-Theoretical and Empirical Study
Re-Evaluating Privacy in Centralized and Decentralized Learning: An Information-Theoretical and Empirical StudyIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024
Changlong Ji
Stephane Maag
Richard Heusdens
Qiongxiu Li
FedML
186
3
0
21 Sep 2024
Perfect Gradient Inversion in Federated Learning: A New Paradigm from
  the Hidden Subset Sum Problem
Perfect Gradient Inversion in Federated Learning: A New Paradigm from the Hidden Subset Sum Problem
Qiongxiu Li
Lixia Luo
Agnese Gini
Changlong Ji
Zhanhao Hu
Xiao-Li Li
Chengfang Fang
Jie Shi
Xiaolin Hu
FedML
244
4
0
21 Sep 2024
A Hybrid Federated Kernel Regularized Least Squares Algorithm
A Hybrid Federated Kernel Regularized Least Squares Algorithm
Celeste Damiani
Yulia Rodina
Sergio Decherchi
FedML
117
5
0
24 Jul 2024
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning
Luca Corbucci
Mikko A. Heikkilä
David Solans Noguero
Anna Monreale
Nicolas Kourtellis
FedML
381
6
0
21 Jul 2024
Provable Privacy Advantages of Decentralized Federated Learning via
  Distributed Optimization
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization
Wenrui Yu
Qiongxiu Li
Milan Lopuhaä-Zwakenberg
Mads Græsbøll Christensen
Richard Heusdens
FedML
183
9
0
12 Jul 2024
FedLog: Personalized Federated Classification with Less Communication
  and More Flexibility
FedLog: Personalized Federated Classification with Less Communication and More Flexibility
Haolin Yu
Guojun Zhang
Pascal Poupart
FedML
192
0
0
11 Jul 2024
QBI: Quantile-based Bias Initialization for Efficient Private Data
  Reconstruction in Federated Learning
QBI: Quantile-based Bias Initialization for Efficient Private Data Reconstruction in Federated Learning
Micha V. Nowak
Tim P. Bott
David Khachaturov
Frank Puppe
Adrian Krenzer
Amar Hekalo
FedML
162
1
0
26 Jun 2024
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in
  Federated Learning
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning
Peng Kuang
Zhiwei Chang
Jiahui Hu
Xiaoyi Pang
Jiacheng Du
Yongle Chen
Kui Ren
FedML
187
9
0
22 Jun 2024
Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic
  Meta-Learning
Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning
Mina Rafiei
Mohammadmahdi Maheri
Hamid R. Rabiee
249
2
0
01 Jun 2024
Federating Dynamic Models using Early-Exit Architectures for Automatic
  Speech Recognition on Heterogeneous Clients
Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous Clients
Mohamed Nabih Ali
Alessio Brutti
Daniele Falavigna
254
1
0
27 May 2024
DAGER: Exact Gradient Inversion for Large Language Models
DAGER: Exact Gradient Inversion for Large Language Models
Ivo Petrov
Dimitar I. Dimitrov
Maximilian Baader
Mark Niklas Muller
Martin Vechev
FedML
192
12
0
24 May 2024
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks
  under Federated Learning, A Survey and Taxonomy
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
196
4
0
16 May 2024
GI-SMN: Gradient Inversion Attack against Federated Learning without
  Prior Knowledge
GI-SMN: Gradient Inversion Attack against Federated Learning without Prior KnowledgeInternational Conference on Intelligent Computing (ICIC), 2024
Jin Qian
Kaimin Wei
Yongdong Wu
Jilian Zhang
Jipeng Chen
Huan Bao
211
6
0
06 May 2024
Privacy-Preserving, Dropout-Resilient Aggregation in Decentralized
  Learning
Privacy-Preserving, Dropout-Resilient Aggregation in Decentralized Learning
Ali Reza Ghavamipour
Benjamin Zi Hao Zhao
Fatih Turkmen
OOD
150
1
0
27 Apr 2024
Confidential Federated Computations
Confidential Federated Computations
Hubert Eichner
Daniel Ramage
Kallista A. Bonawitz
Dzmitry Huba
Tiziano Santoro
...
Albert Cheu
Katharine Daly
Adria Gascon
Marco Gruteser
Brendan McMahan
386
10
0
16 Apr 2024
pfl-research: simulation framework for accelerating research in Private
  Federated Learning
pfl-research: simulation framework for accelerating research in Private Federated LearningNeural Information Processing Systems (NeurIPS), 2024
Filip Granqvist
Congzheng Song
Áine Cahill
Rogier van Dalen
Martin Pelikan
Yi Sheng Chan
Xiaojun Feng
Natarajan Krishnaswami
Vojta Jina
Mona Chitnis
FedML
228
13
0
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Privacy Backdoors: Enhancing Membership Inference through Poisoning
  Pre-trained Models
Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models
Yuxin Wen
Leo Marchyok
Sanghyun Hong
Jonas Geiping
Tom Goldstein
Nicholas Carlini
SILMAAML
275
28
0
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Privacy Backdoors: Stealing Data with Corrupted Pretrained Models
Privacy Backdoors: Stealing Data with Corrupted Pretrained Models
Shanglun Feng
Florian Tramèr
SILM
261
30
0
30 Mar 2024
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from
  Federated Learning
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning
Joshua C. Zhao
Ahaan Dabholkar
Atul Sharma
Saurabh Bagchi
FedML
185
3
0
26 Mar 2024
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