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Federated learning (FL) enables collaborative model training while preserving data privacy, but its decentralized nature exposes it to client-side data poisoning attacks (DPAs) and model poisoning attacks (MPAs) that degrade global model performance.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Gradient-based learning applied to document recognition
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Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Auror: Defending against poisoning attacks in collaborative deep learning systems
Shiqi Shen, Shruti Tople, and Prateek Saxena · 2016
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Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
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Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 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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Sebastian U Stich, Jean-Baptiste Cordonnier, and Martin Jaggi · 2018
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Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett · 2018
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A little is enough: Circumventing defenses for distributed learning
Gilad Baruch, Moran Baruch, and Yoav Goldberg · 2019
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Analyzing federated learning through an adversarial lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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Communication-efficient distributed sgd with sketching
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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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Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan · 2019
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Dba: Distributed backdoor attacks against federated learning
Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li · 2019
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Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni · 2019
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How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2020
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Fltrust: Byzantine-robust federated learning via trust bootstrapping
Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong · 2020
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Byzantine-robust learning on heterogeneous datasets via bucketing
Sai Praneeth Karimireddy, Lie He, and Martin Jaggi · 2022
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Privacy and robustness in federated learning: Attacks and defenses
Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen, Lichao Sun, Jun Zhao, Qiang Yang, and S Yu Philip · 2022
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Federated learning for smart healthcare: A survey
Dinh C Nguyen, Quoc-Viet Pham, Pubudu N Pathirana, Ming Ding, Aruna Seneviratne, Zihuai Lin, Octavia Dobre, and Won-Joo Hwang · 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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Federated evaluation and tuning for on-device personalization: System design & applications, 2022
Matthias Paulik, Matt Seigel, Henry Mason, Dominic Telaar, Joris Kluivers, Rogier van Dalen, Chi Wai Lau, Luke Carlson, Filip Granqvist, Chris Vandevelde, Sudeep Agarwal, Julien Freudiger, Andrew Byde, Abhishek Bhowmick, Gaurav Kapoor, Si Beaumont, Áine Cahill, Dominic Hughes, Omid Javidbakht, Fei Dong, Rehan Rishi, and Stanley Hung · 2022
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Utilization of fate in risk management of credit in small and micro enterprises, 2020
editor2fedai · 2020
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Local model poisoning attacks to { \{ Byzantine-Robust } \} federated learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong · 2020
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The limitations of federated learning in sybil settings
Clement Fung, Chris JM Yoon, and Ivan Beschastnikh · 2020
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Ardis: a swedish historical handwritten digit dataset
Huseyin Kusetogullari, Amir Yavariabdi, Abbas Cheddad, Håkan Grahn, and Johan Hall · 2020
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Federated learning in mobile edge networks: A comprehensive survey
Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, and Chunyan Miao · 2020
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Attack of the tails: Yes, you really can backdoor federated learning
Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, and Dimitris Papailiopoulos · 2020
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Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
Cited alongside, same era.
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Robust aggregation for federated learning
Krishna Pillutla, Sham M Kakade, and Zaid Harchaoui · 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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Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning
Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, and Daniel Ramage · 2022
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A comprehensive survey on poisoning attacks and countermeasures in machine learning
Zhiyi Tian, Lei Cui, Jie Liang, and Shui Yu · 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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Neurotoxin: Durable backdoors in federated learning
Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael Mahoney, Prateek Mittal, Ramchandran Kannan, and Joseph Gonzalez · 2022
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3dfed: Adaptive and extensible framework for covert backdoor attack in federated learning
Haoyang Li, Qingqing Ye, Haibo Hu, Jin Li, Leixia Wang, Chengfang Fang, and Jie Shi · 2023
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An experimental study of byzantine-robust aggregation schemes in federated learning
Shenghui Li, Edith C-H Ngai, and Thiemo Voigt · 2023
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Sok: Systematizing attack studies in federated learning–from sparseness to completeness
Geetanjli Sharma, MAP Chamikara, Mohan Baruwal Chhetri, and Yi-Ping Phoebe Chen · 2023
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Heterogeneous federated learning: State-of-the-art and research challenges
Mang Ye, Xiuwen Fang, Bo Du, Pong C Yuen, and Dacheng Tao · 2023
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Accessed: 2024-10-03
California consumer privacy act (ccpa), state of california - department of justice - office of the attorney general, oct 2018 · 2024
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Accessed: 2024-10-03
General data protection regulation (gdpr), apr 2024 · 2024
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Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey
Yichen Wan, Youyang Qu, Wei Ni, Yong Xiang, Longxiang Gao, and Ekram Hossain · 2024
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Jailbreak attacks and defenses against large language models: A survey
Sibo Yi, Yule Liu, Zhen Sun, Tianshuo Cong, Xinlei He, Jiaxing Song, Ke Xu, and Qi Li · 2024
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