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Federated learning (FL) provides an efficient paradigm to jointly train a global model leveraging data from distributed users.
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Measuring the effects of non-identical data distribution for federated visual classification
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Certified Robustness to Adversarial Examples with Differential Privacy. In 2019 IEEE Symposium on Security and Privacy (SP)
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Data Poisoning against Differentially-Private Learners: Attacks and Defenses. In International Joint Conference on Artificial Intelligence
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Deep partition aggregation: Provable defense against general poisoning attacks
Alexander Levine and Soheil Feizi. 2021 · 2021
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Projected federated averaging with heterogeneous differential privacy
Junxu Liu, Jian Lou, Li Xiong, Jinfei Liu, and Xiaofeng Meng. 2021 · 2021
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Dopamine: Differentially Private Federated Learning on Medical Data
Mohammad Malekzadeh, Burak Hasircioglu, Nitish Mital, Kunal Katarya, Mehmet Emre Ozfatura, and Deniz Gunduz. 2021 · 2021
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Opacus – Train PyTorch models with Differential Privacy
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Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning. In NDSS
Virat Shejwalkar and Amir Houmansadr. 2021 · 2021
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Certified robustness to word substitution attack with differential privacy. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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Crfl: Certifiably robust federated learning against backdoor attacks. In International Conference on Machine Learning
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Voting-based Approaches For Differentially Private Federated Learning
Yuqing Zhu, Xiang Yu, Yi-Hsuan Tsai, Francesco Pittaluga, Masoud Faraki, Manmohan Chandraker, and Yu-Xiang Wang. 2021 · 2021
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Differentially Private Federated Learning with Local Regularization and Sparsification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Anda Cheng, Peisong Wang, Xi Sheryl Zhang, and Jian Cheng. 2022 · 2022
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Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks. AAAI
Jinyuan Jia, Yupei Liu, Xiaoyu Cao, and Neil Zhenqiang Gong. 2022 · 2022
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Certifiably Robust Interpretation via Rényi Differential Privacy
Ao Liu, Xiaoyu Chen, Sijia Liu, Lirong Xia, and Chuang Gan. 2022a · 2022
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On privacy and personalization in cross-silo federated learning
Ken Liu, Shengyuan Hu, Steven Z Wu, and Virginia Smith. 2022b · 2022
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Local and Central Differential Privacy for Robustness and Privacy in Federated Learning
Mohammad Naseri, Jamie Hayes, and Emiliano De Cristofaro. 2022 · 2022
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{ \{ FLAME } \} : Taming backdoors in federated learning. In USENIX Security Symposium
Thien Duc Nguyen, Phillip Rieger, Roberta De Viti, Huili Chen, et al · 2022
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Differentially private federated learning on heterogeneous data. In International Conference on Artificial Intelligence and Statistics
Maxence Noble, Aurélien Bellet, and Aymeric Dieuleveut. 2022 · 2022
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Improved certified defenses against data poisoning with (deterministic) finite aggregation. In International Conference on Machine Learning
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ZenoPS: A Distributed Learning System Integrating Communication Efficiency and Security
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SoK: Certified Robustness for Deep Neural Networks. In 2023 IEEE Symposium on Security and Privacy (SP)
Linyi Li, Tao Xie, and Bo Li. 2022 · 2023
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Distributed differential privacy for federated learning
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How Apple personalizes Siri without hoovering up your data
MIT Technology Review. 2019 · 2023
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Make Landscape Flatter in Differentially Private Federated Learning
Yifan Shi, Yingqi Liu, Kang Wei, Li Shen, Xueqian Wang, and Dacheng Tao. 2023 · 2023
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Rab: Provable robustness against backdoor attacks
Maurice Weber, Xiaojun Xu, Bojan Karlaš, Ce Zhang, and Bo Li. 2023 · 2023
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PRIVATEFL: Accurate, Differentially Private Federated Learning via Personalized Data Transformation. In USENIX Security Symposium
Yuchen Yang, Bo Hui, Haolin Yuan, Neil Gong, and Yinzhi Cao. 2023 · 2023
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