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In Federated Learning (FL), models are as fragile as centrally trained models against adversarial examples.
Bayesian Nonparametric Federated Learning of Neural Networks
Yurochkin, M.; Agarwal, M.; Ghosh, S.; Greenewald, K.; Hoang, T. N.; and Khazaeni, Y. 2019 · 1905
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On the Convergence of FedAvg on Non-IID Data
Li, X.; Huang, K.; Yang, W.; Wang, S.; and Zhang, Z. 2020 · 1907
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Square Attack: a query-efficient black-box adversarial attack via random search
Andriushchenko, M.; Croce, F.; Flammarion, N.; and Hein, M. 2020 · 1912
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Advances and Open Problems in Federated Learning
Kairouz, P. 2021 · 1912
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Gradient-based learning applied to document recognition
Lecun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998 · 1998
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Croce, F.; and Hein, M. 2020 · 2003
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Tackling the objective inconsistency problem in heterogeneous federated optimization
Wang, J.; Liu, Q.; Liang, H.; Joshi, G.; and Poor, H. V. 2020 · 2007
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A.; and Hinton, G. 2009 · 2009
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FAT: Federated Adversarial Training
Zizzo, G.; Rawat, A.; Sinn, M.; and Buesser, B. 2020 · 2012
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Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
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Tiny imagenet visual recognition challenge
Le, Y.; and Yang, X. 2015 · 2015
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Towards evaluating the robustness of neural networks
Carlini, N.; and Wagner, D. 2017 · 2017
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Adversarial examples in the physical world
Kurakin, A.; Goodfellow, I.; and Bengio, S. 2017 · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2017 · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Xiao, H.; Rasul, K.; and Vollgraf, R. 2017 · 2017
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Boosting adversarial attacks with momentum
Dong, Y.; Liao, F.; Pang, T.; Su, H.; Zhu, J.; Hu, X.; and Li, J. 2018 · 2018
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Knowledge Distillation with Adversarial Samples Supporting Decision Boundary
Heo, B.; Lee, M.; Yun, S.; and Choi, J. Y. 2018 · 2018
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Kannan, H.; Kurakin, A.; and Goodfellow, I. 2018 · 2018
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Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning
Chen, Z.; Zhu, M.; Yang, C.; and Yuan, Y. 2021b · 2021
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Learnable boundary guided adversarial training
Cui, J.; Liu, S.; Wang, L.; and Jia, J. 2021 · 2021
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Where and How to Transfer: Knowledge Aggregation-Induced Transferability Perception for Unsupervised Domain Adaptation
Dong, J.; Cong, Y.; Sun, G.; Fang, Z.; and Ding, Z. 2021 · 2021
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Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning
Hong, J.; Wang, H.; Wang, Z.; and Zhou, J. 2021 · 2021
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FedRec++: Lossless Federated Recommendation with Explicit Feedback
Liang, F.; Pan, W.; and Ming, Z. 2021 · 2021
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Federated optimization in heterogeneous networks
Li, T.; Sahu, A. K.; Zaheer, M.; Sanjabi, M.; Talwalkar, A.; and Smith, V. 2018 · 2018
Cited alongside, same era.
Federated Learning with Non-IID Data
Zhao, Y.; Li, M.; Lai, L.; Suda, N.; Civin, D.; and Chandra, V. 2018 · 2018
Cited alongside, same era.
SuperVAE: Superpixelwise Variational Autoencoder for Salient Object Detection
Li, B.; Sun, Z.; and Guo, Y. 2019 · 2019
Cited alongside, same era.
Detecting Robust Co-Saliency with Recurrent Co-Attention Neural Network
Li, B.; Sun, Z.; Tang, L.; Sun, Y.; and Shi, J. 2019 · 2019
Cited alongside, same era.
Theoretically principled trade-off between robustness and accuracy
Zhang, H.; Yu, Y.; Jiao, J.; Xing, E.; El Ghaoui, L.; and Jordan, M. 2019 · 2019
Cited alongside, same era.
MMA Training: Direct Input Space Margin Maximization through Adversarial Training
Ding, G. W.; Sharma, Y.; Lui, K. Y. C.; and Huang, R. 2020 · 2020
Cited alongside, same era.
SCAFFOLD: Stochastic controlled averaging for federated learning
Karimireddy, S. P.; Kale, S.; Mohri, M.; Reddi, S.; Stich, S.; and Suresh, A. T. 2020 · 2020
Cited alongside, same era.
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
Liu, Q.; Chen, C.; Qin, J.; Dou, Q.; and Heng, P. 2021a · 2021
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FedCT: Federated Collaborative Transfer for Recommendation
Liu, S.; Xu, S.; Yu, W.; Fu, Z.; Zhang, Y.; and Marian, A. 2021b · 2021
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Adversarial training in communication constrained federated learning
Shah, D.; Dube, P.; Chakraborty, S.; and Verma, A. 2021 · 2021
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A Secure and Efficient Federated Learning Framework for NLP
Wang, C.; Deng, J.; Meng, X.; Wang, Y.; Li, J.; Miao, F.; Rajasekaran, S.; and Ding, C. 2021 · 2021
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Geometry-aware Instance-reweighted Adversarial Training
Zhang, J.; Zhu, J.; Niu, G.; Han, B.; Sugiyama, M.; and Kankanhalli, M. 2021 · 2021
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Gear: a margin-based federated adversarial training approach
Chen, C.; Zhang, J.; and Lyu, L. 2022 · 2022
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Shape Matters: Deformable Patch Attack
Chen, Z.; Li, B.; Wu, S.; Xu, J.; Ding, S.; and Zhang, W. 2022b · 2022
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Privacy and robustness in federated learning: Attacks and defenses
Lyu, L.; Yu, H.; Ma, X.; Chen, C.; Sun, L.; Zhao, J.; Yang, Q.; and Philip, S. Y. 2022 · 2022
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Adversarial robustness through bias variance decomposition: A new perspective for federated learning
Zhou, Y.; Wu, J.; Wang, H.; and He, J. 2022 · 2022
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