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\textit{Federated learning} (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy.
Gradient-Based Learning Applied to Document Recognition
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Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent. In
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Communication-Efficient Learning of Deep Networks from Decentralized Data. In
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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017 · 2017
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Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates. In
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter L. Bartlett. 2018 · 2018
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A Little Is Enough: Circumventing Defenses for Distributed Learning. In
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Sever: A Robust Meta-Algorithm for Stochastic Optimization. In
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Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging
Luis Muñoz-González, Kenneth T. Co, and Emil C. Lupu. 2019 · 2019
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FABA: An Algorithm for Fast Aggregation against Byzantine Attacks in Distributed Neural Networks. In
Qi Xia, Zeyi Tao, Zijiang Hao, and Qun Li. 2019 · 2019
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Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance. In
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Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning. In
Yae Jee Cho, Samarth Gupta, Gauri Joshi, and Osman Yağan. 2020 · 2020
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Local Model Poisoning Attacks to Byzantine-Robust Federated Learning. In
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong. 2020 · 2020
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The Limitations of Federated Learning in Sybil Settings. In
Clement Fung, Chris J. M. Yoon, and Ivan Beschastnikh. 2020 · 2020
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Mitigating Byzantine Attacks in Federated Learning
Saurav Prakash and Amir Salman Avestimehr. 2020 · 2020
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Data Poisoning Attacks Against Federated Learning Systems. In
Vale Tolpegin, Stacey Truex, Mehmet Emre Gursoy, and Ling Liu. 2020 · 2020
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Shielding Federated Learning: A New Attack Approach and Its Defense. In
Wei Wan, Jianrong Lu, Shengshan Hu, Leo Yu Zhang, and Xiaobing Pei. 2021 · 2021
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Adaptive Client Selection in Resource Constrained Federated Learning Systems: A Deep Reinforcement Learning Approach
Hangjia Zhang, Zhijun Xie, Roozbeh Zarei, Tao Wu, and Kewei Chen. 2021b · 2021
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Client Selection for Federated Learning with non-IID Data in Mobile Edge Computing
Wenyu Zhang, Xiumin Wang, Pan Zhou, Weiwei Wu, and Xinglin Zhang. 2021a · 2021
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MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients. In
Xiaoyu Cao and Neil Zhenqiang Gong. 2022 · 2022
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Shielding Federated Learning: Mitigating Byzantine Attacks with Less Constraints. In
Minghui Li, Wei Wan, Jianrong Lu, Shengshan Hu, Junyu Shi, Leo Yu Zhang, Man Zhou, and Yifeng Zheng. 2022 · 2022
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Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation. In
Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta. 2020 · 2020
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CONTRA: Defending Against Poisoning Attacks in Federated Learning. In
Sana Awan, Bo Luo, and Fengjun Li. 2021 · 2021
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FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping. In
Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong. 2021 · 2021
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Learning from History for Byzantine Robust Optimization. In
Sai Praneeth Karimireddy, Lie He, and Martin Jaggi. 2021 · 2021
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Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent. In
El Mahdi El Mhamdi, Rachid Guerraoui, and Sébastien Rouault. 2021 · 2021
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Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning. In
Virat Shejwalkar and Amir Houmansadr. 2021 · 2021
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SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. In
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Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features. In
Xinyi Shang, Yang Lu, Gang Huang, and Hanzi Wang. 2022 · 2022
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Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning. In
Junyu Shi, Wei Wan, Shengshan Hu, Jianrong Lu, and Leo Yu Zhang. 2022 · 2022
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Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection. In
Wei Wan, Shengshan Hu, Jianrong Lu, Leo Yu Zhang, Hai Jin, and Yuanyuan He. 2022 · 2022
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FedInv: Byzantine-Robust Federated Learning by Inversing Local Model Updates. In
Bo Zhao, Peng Sun, Tao Wang, and Keyu Jiang. 2022 · 2022
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Hangtao Zhang, Zeming Yao, Leo Yu Zhang, Shengshan Hu, Chao Chen, Alan Liew, and Zhetao Li. 2023 · 2023
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