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Recent studies have shown that, like traditional machine learning, federated learning (FL) is also vulnerable to adversarial attacks.
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2018
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Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2018
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Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, and John C Duchi · 2019
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Real-world image datasets for federated learning
Jiahuan Luo, Xueyang Wu, Yun Luo, Anbu Huang, Yunfeng Huang, Yang Liu, and Qiang Yang · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 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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Theoretically principled trade-off between robustness and accuracy
Fat: Federated adversarial training
Giulio Zizzo, Ambrish Rawat, Mathieu Sinn, and Beat Buesser · 2020
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Federated robustness propagation: Sharing adversarial robustness in federated learning
Junyuan Hong, Haotao Wang, Zhangyang Wang, and Jiayu Zhou · 2021
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Anti-backdoor learning: Training clean models on poisoned data
Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, and Xingjun Ma · 2021
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Imbalanced adversarial training with reweighting
Wentao Wang, Han Xu, Xiaorui Liu, Yaxin Li, Bhavani Thuraisingham, and Jiliang Tang · 2021
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Adversarial robustness under long-tailed distribution
Tong Wu, Ziwei Liu, Qingqiu Huang, Yu Wang, and Dahua Lin · 2021
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Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
Cited alongside, same era.
The non-iid data quagmire of decentralized machine learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip Gibbons · 2020
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Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Cited alongside, same era.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Cited alongside, same era.
Privacy and robustness in federated learning: Attacks and defenses
Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen, Lichao Sun, Jun Zhao, Qiang Yang, and Philip S Yu · 2020
Cited alongside, same era.
Threats to federated learning
Lingjuan Lyu, Han Yu, Jun Zhao, and Qiang Yang · 2020
Cited alongside, same era.
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To be robust or to be fair: Towards fairness in adversarial training
Han Xu, Xiaorui Liu, Yaxin Li, Anil Jain, and Jiliang Tang · 2021
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Geometry-aware instance-reweighted adversarial training
Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, and Mohan Kankanhalli · 2021
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Adversarially robust federated learning for neural networks, 2021
Yao Zhou, Jun Wu, and Jingrui He · 2021
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Federated learning on non-iid data: A survey
Hangyu Zhu, Jinjin Xu, Shiqing Liu, and Yaochu Jin · 2021
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Decision boundary-aware data augmentation for adversarial training
Chen Chen, Jingfeng Zhang, Xilie Xu, Lingjuan Lyu, Chaochao Chen, Tianlei Hu, and Gang Chen · 2022
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Fedproto: Federated prototype learning across heterogeneous clients
Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, and Chengqi Zhang · 2022
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Federated learning from pre-trained models: A contrastive learning approach
Yue Tan, Guodong Long, Jie Ma, Lu Liu, Tianyi Zhou, and Jing Jiang · 2022
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Qekd: Query-efficient and data-free knowledge distillation from black-box models
Jie Zhang, Chen Chen, Jiahua Dong, Ruoxi Jia, and Lingjuan Lyu · 2022
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Towards efficient data free black-box adversarial attack
Jie Zhang, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Lei Zhang, and Chao Wu · 2022
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Federated learning with label distribution skew via logits calibration
Jie Zhang, Zhiqi Li, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, and Chao Wu · 2022
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