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Federated Learning (FL) systems are susceptible to adversarial attacks, such as model poisoning attacks and backdoor attacks.
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P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer · 2017
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Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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Byzantine-robust distributed learning: Towards optimal statistical rates
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Rachid Guerraoui, Sébastien Rouault, et al · 2018
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The hidden vulnerability of distributed learning in byzantium
Rachid Guerraoui, Sébastien Rouault, et al · 2018
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Leaf: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
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Federated learning of out-of-vocabulary words
Mingqing Chen, Rajiv Mathews, Tom Ouyang, and Françoise Beaufays · 2019
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Federated learning for emoji prediction in a mobile keyboard
Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, and Françoise Beaufays · 2019
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Federated learning for keyword spotting
David Leroy, Alice Coucke, Thibaut Lavril, Thibault Gisselbrecht, and Joseph Dureau · 2019
Gradient disaggregation: Breaking privacy in federated learning by reconstructing the user participant matrix
Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay apa Reddi, and Michael Mitzenmacher · 2021
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Cafe: Catastrophic data leakage in vertical federated learning
Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, and Tianyi Chen · 2021
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Defending against backdoors in federated learning with robust learning rate
Mustafa Safa Ozdayi, Murat Kantarcioglu, and Yulia R Gel · 2021
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Fl-wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective
Jingwei Sun, Ang Li, Louis DiValentin, Amin Hassanzadeh, Yiran Chen, and Hai Li · 2021
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Mystique: Efficient conversions for { \{ Zero-Knowledge } \} proofs with applications to machine learning
Chenkai Weng, Kang Yang, Xiang Xie, Jonathan Katz, and Xiao Wang · 2021
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Cited alongside, same era.
Analyzing federated learning through an adversarial lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo · 2019
Cited alongside, same era.
Model poisoning attacks against distributed machine learning systems
Richard Tomsett, Kevin Chan, and Supriyo Chakraborty · 2019
Cited alongside, same era.
Byzantine-resilient stochastic gradient descent for distributed learning: A Lipschitz-inspired coordinate-wise median approach
H. Yang, X. Zhang, M. Fang, and J. Liu · 2019
Cited alongside, same era.
Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan · 2019
Cited alongside, same era.
Attack-resistant federated learning with residual-based reweighting
Shuhao Fu, Chulin Xie, Bo Li, and Qifeng Chen · 2019
Cited alongside, same era.
An iterative scheme for leverage-based approximate aggregation
S. Han, H. Wang, J. Wan, and J. Li · 2019
Cited alongside, same era.
Clinical epidemiology: the essentials
Grant S Fletcher · 2019
Cited alongside, same era.
ZkCNN: Zero knowledge proofs for convolutional neural network predictions and accuracy
T. Liu, X. Xie, and Y. Zhang · 2021
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A review of medical federated learning: Applications in oncology and cancer research
Alexander Chowdhury, Hasan Kassem, Nicolas Padoy, Renato Umeton, and Alexandros Karargyris · 2022
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Mpaf: Model poisoning attacks to federated learning based on fake clients
Xiaoyu Cao and Neil Zhenqiang Gong · 2022
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Cocktail party attack: Breaking aggregation-based privacy in federated learning using independent component analysis
Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong, G. Edward Suh, Moinuddin K. Qureshi, and Hsien-Hsin S. Lee · 2022
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Neurotoxin: Durable backdoors in federated learning
Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael W. Mahoney, Joseph Gonzalez, Kannan Ramchandran, and Prateek Mittal · 2022
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Robust aggregation for federated learning
Krishna Pillutla, Sham M Kakade, and Zaid Harchaoui · 2022
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Byzantine-robust decentralized learning via self-centered clipping, 2022
L. He, S. P. Karimireddy, and M. Jaggi · 2022
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Flcert: Provably secure federated learning against poisoning attacks
Xiaoyu Cao, Zaixi Zhang, Jinyuan Jia, and Neil Zhenqiang Gong · 2022
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{ \{ FLAME } \} : Taming backdoors in federated learning
Thien Duc Nguyen, Phillip Rieger, Roberta De Viti, Huili Chen, Björn B Brandenburg, Hossein Yalame, Helen Möllering, Hossein Fereidooni, Samuel Marchal, Markus Miettinen, et al · 2022
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Deepsight: Mitigating backdoor attacks in federated learning through deep model inspection
Phillip Rieger, Thien Duc Nguyen, Markus Miettinen, and Ahmad-Reza Sadeghi · 2022
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Flip: A provable defense framework for backdoor mitigation in federated learning
Kaiyuan Zhang, Guanhong Tao, Qiuling Xu, Siyuan Cheng, Shengwei An, Yingqi Liu, Shiwei Feng, Guangyu Shen, Pin-Yu Chen, Shiqing Ma, et al · 2022
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Pass: Parameters audit-based secure and fair federated learning scheme against free rider
Jianhua Wang · 2022
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Circom zkSNARK ecosystem, 2022
Circom Contributors · 2022
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Fldetector: Defending federated learning against model poisoning attacks via detecting malicious clients
Zaixi Zhang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong · 2022
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Implementation of fldetector
Zaixi Zhang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong · 2022
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Baybfed: Bayesian backdoor defense for federated learning
Kavita Kumari, Phillip Rieger, Hossein Fereidooni, Murtuza Jadliwala, and Ahmad-Reza Sadeghi · 2023
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Hao Yu, Chuan Ma, Meng Liu, Xinwang Liu, Zhe Liu, and Ming Ding · 2023
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Nspfl: A novel secure and privacy-preserving federated learning with data integrity auditing
Zehu Zhang and Yanping Li · 2024
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Resisting backdoor attacks in federated learning via bidirectional elections and individual perspective
Zhen Qin, Feiyi Chen, Chen Zhi, Xueqiang Yan, and Shuiguang Deng · 2024
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