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Federated learning has become a widely used paradigm for collaboratively training a common model among different participants with the help of a central server that coordinates the training.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Stealthy poisoning attacks on PCA-based anomaly detectors
Benjamin IP Rubinstein, Blaine Nelson, Ling Huang, Anthony D Joseph, Shing-hon Lau, Satish Rao, Nina Taft, and JD Tygar. 2009 · 2009
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes. 2010 · 2010
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I Have a DREAM! (DiffeRentially privatE smArt Metering). In IH
Gergely Ács and Claude Castelluccia. 2011 · 2011
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Poisoning Attacks against Support Vector Machines. In Proceedings of the 29th International Coference on International Conference on Machine Learning (Edinburgh, Scotland) (ICML’12) . Omnipress, Madison, WI, USA, 1467–1474
Battista Biggio, Blaine Nelson, and Pavel Laskov. 2012 · 2012
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Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (Austin, Texas) (AAAI’15) . AAAI Press, 2871–2877
Shike Mei and Xiaojin Zhu. 2015 · 2015
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Privacy-Preserving Deep Learning. In CCS
Reza Shokri and Vitaly Shmatikov. 2015 · 2015
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Is feature selection secure against training data poisoning?. In International Conference on Machine Learning . PMLR, 1689–1698
Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, and Fabio Roli. 2015 · 2015
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Practical secure aggregation for federated learning on user-held data
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2016 · 2016
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Communication-Efficient Learning of Deep Networks from Decentralized Data. In AISTATS
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2016 · 2016
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Auror: Defending against poisoning attacks in collaborative deep learning systems. In Proceedings of the 32nd Annual Conference on Computer Security Applications . 508–519
Shiqi Shen, Shruti Tople, and Prateek Saxena. 2016 · 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 · 2017
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Practical Secure Aggregation for Privacy-Preserving Machine Learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS 2017, Dallas, TX, USA, October 30 - November 03, 2017 , Bhavani M. Thuraisingham, David Evans, Tal Malkin, and Dongyan Xu (Eds.). ACM, 1175–1191
Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017 · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song. 2017 · 2017
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Understanding black-box predictions via influence functions. In International Conference on Machine Learning . PMLR, 1885–1894
Pang Wei Koh and Percy Liang. 2017 · 2017
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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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How To Backdoor Federated Learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2018 · 2018
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signSGD with majority vote is communication efficient and fault tolerant
Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, and Anima Anandkumar. 2018 · 2018
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The Hidden Vulnerability of Distributed Learning in Byzantium. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80) , Jennifer Dy and Andreas Krause (Eds.). PMLR, 3521–3530
El Mahdi El Mhamdi, Rachid Guerraoui, and Sébastien Rouault. 2018 · 2018
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Mitigating sybils in federated learning poisoning
Clement Fung, Chris JM Yoon, and Ivan Beschastnikh. 2018 · 2018
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Federated Learning for Mobile Keyboard Prediction
Andrew Hard, Chloé M Kiddon, Daniel Ramage, Francoise Beaufays, Hubert Eichner, Kanishka Rao, Rajiv Mathews, and Sean Augenstein. 2018 · 2018
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Manipulating machine learning: Poisoning attacks and countermeasures for regression learning. In 2018 IEEE Symposium on Security and Privacy (SP) . IEEE, 19–35
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li. 2018 · 2018
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Sok: Security and privacy in machine learning. In 2018 IEEE European Symposium on Security and Privacy (EuroS&P) . IEEE, 399–414
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael P Wellman. 2018 · 2018
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Generalized byzantine-tolerant sgd
Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta. 2018 · 2018
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Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80) , Jennifer Dy and Andreas Krause (Eds.). PMLR, 5650–5659
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett. 2018 · 2018
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A Little Is Enough: Circumventing Defenses For Distributed Learning. In Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32. Curran Associates, Inc
Gilad Baruch, Moran Baruch, and Yoav Goldberg. 2019 · 2019
Cited alongside, same era.
Analyzing federated learning through an adversarial lens. In International Conference on Machine Learning . PMLR, 634–643
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo. 2019 · 2019
Cited alongside, same era.
Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer
Hongyan Chang, Virat Shejwalkar, Reza Shokri, and Amir Houmansadr. 2019 · 2019
Cited alongside, same era.
Differential privacy-enabled federated learning for sensitive health data
Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis, Issa Sylla, Yoonyoung Park, Grace Hsu, and Amar Das. 2019 · 2019
Cited alongside, same era.
When the curious abandon honesty: Federated learning is not private
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, and Nicolas Papernot. 2021 · 2021
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Versa: Verifiable secure aggregation for cross-device federated learning
Changhee Hahn, Hodong Kim, Minjae Kim, and Junbeom Hur. 2021 · 2021
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PFLM: Privacy-preserving federated learning with membership proof
Changsong Jiang, Chunxiang Xu, and Yuan Zhang. 2021 · 2021
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Privacy-Preserving and Bandwidth-Efficient Federated Learning: An Application to in-Hospital Mortality Prediction. In Proceedings of the Conference on Health, Inference, and Learning (Virtual Event, USA) (CHIL ’21) . Association for Computing Machinery, New York, NY, USA, 25–35
Raouf Kerkouche, Gergely Ács, Claude Castelluccia, and Pierre Genevès. 2021 · 2021
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Exploiting unintended feature leakage in collaborative learning. In 2019 IEEE symposium on security and privacy (SP) . IEEE, 691–706
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2019 · 2019
Cited alongside, same era.
Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning. In 2019 IEEE symposium on security and privacy (SP) . IEEE, 739–753
Milad Nasr, Reza Shokri, and Amir Houmansadr. 2019 · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Potential uses for the Privacy Sandbox
Justin Schuh. 2019 · 2019
Cited alongside, same era.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks. In 2019 IEEE Symposium on Security and Privacy (SP) . IEEE, 707–723
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao. 2019 · 2019
Cited alongside, same era.
Verifynet: Secure and verifiable federated learning
Guowen Xu, Hongwei Li, Sen Liu, Kan Yang, and Xiaodong Lin. 2019 · 2019
Cited alongside, same era.
Bayesian nonparametric federated learning of neural networks. In International conference on machine learning . PMLR, 7252–7261
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni. 2019 · 2019
Cited alongside, same era.
Ligeng Zhu, Zhijian Liu, and Song Han. 2019 · 2019
Cited alongside, same era.
Gradient disaggregation: Breaking privacy in federated learning by reconstructing the user participant matrix. In International Conference on Machine Learning . PMLR, 5959–5968
Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, and Michael Mitzenmacher. 2021 · 2021
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A secure federated learning framework using homomorphic encryption and verifiable computing. In 2021 Reconciling Data Analytics, Automation, Privacy, and Security: A Big Data Challenge (RDAAPS) . IEEE, 1–8
Abbass Madi, Oana Stan, Aurélien Mayoue, Arnaud Grivet-Sébert, Cédric Gouy-Pailler, and Renaud Sirdey. 2021 · 2021
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A verifiable federated learning scheme based on secure multi-party computation. In Wireless Algorithms, Systems, and Applications: 16th International Conference, WASA 2021, Nanjing, China, June 25–27, 2021, Proceedings, Part II . Springer, 198–209
Wenhao Mou, Chunlei Fu, Yan Lei, and Chunqiang Hu. 2021 · 2021
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A federated learning aggregation algorithm for pervasive computing: Evaluation and comparison. In 2021 IEEE International Conference on Pervasive Computing and Communications (PerCom) . IEEE, 1–10
EK Sannara, Francois Portet, Philippe Lalanda, and VEGA German. 2021 · 2021
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Data-Free Model Extraction. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021 . Computer Vision Foundation / IEEE, 4771–4780
Jean-Baptiste Truong, Pratyush Maini, Robert J. Walls, and Nicolas Papernot. 2021 · 2021
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All You Need Is Matplotlib, or Federated Learning with Untrusted Servers is Not Private
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, and Nicolas Papernot. 2022 · 2022
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Robbing the fed: Directly obtaining private data in federated learning with modified models
Liam Fowl, Jonas Geiping, Wojtek Czaja, Micah Goldblum, and Tom Goldstein. 2022 · 2022
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Label Inference Attacks Against Vertical Federated Learning. In 31st USENIX Security Symposium (USENIX Security 22) . USENIX Association, Boston, MA
Chong Fu, Xuhong Zhang, Shouling Ji, Jinyin Chen, Jingzheng Wu, Shanqing Guo, Jun Zhou, Alex X Liu, and Ting Wang. 2022 · 2022
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Verifiable and privacy preserving federated learning without fully trusted centers
Gang Han, Tiantian Zhang, Yinghui Zhang, Guowen Xu, Jianfei Sun, and Jin Cao. 2022 · 2022
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Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage
Zhuohang Li, Jiaxin Zhang, Luyang Liu, and Jian Liu. 2022 · 2022
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ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning
Zhuoran Ma, Jianfeng Ma, Yinbin Miao, Yingjiu Li, and Robert H. Deng. 2022 · 2022
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FLAME: Taming Backdoors in Federated Learning. In 31st USENIX Security Symposium, USENIX Security 2022, Boston, MA, USA, August 10-12, 2022 , Kevin R. B. Butler and Kurt Thomas (Eds.). USENIX Association, 1415–1432
Thien Duc Nguyen, Phillip Rieger, Huili Chen, Hossein Yalame, Helen Möllering, Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Shaza Zeitouni, Farinaz Koushanfar, Ahmad-Reza Sadeghi, and Thomas Schneider. 2022 · 2022
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Eluding Secure Aggregation in Federated Learning via Model Inconsistency. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (Los Angeles, CA, USA) (CCS ’22) . Association for Computing Machinery, New York, NY, USA, 2429–2443
Dario Pasquini, Danilo Francati, and Giuseppe Ateniese. 2022 · 2022
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DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection. In 29th Annual Network and Distributed System Security Symposium, NDSS 2022, San Diego, California, USA, April 24-28, 2022 . The Internet Society
Phillip Rieger, Thien Duc Nguyen, Markus Miettinen, and Ahmad-Reza Sadeghi. 2022 · 2022
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User-Level Label Leakage from Gradients in Federated Learning
Aidmar Wainakh, Fabrizio Ventola, Till Müßig, Jens Keim, Carlos Garcia Cordero, Ephraim Zimmer, Tim Grube, Kristian Kersting, and Max Mühlhäuser. 2022 · 2022
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Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification. In Proceedings of the 39th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 162) , Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato (Eds.). PMLR, 23668–23684
Yuxin Wen, Jonas A. Geiping, Liam Fowl, Micah Goldblum, and Tom Goldstein. 2022 · 2022
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Towards Verifiable Federated Learning. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 , Lud De Raedt (Ed.). International Joint Conferences on Artificial Intelligence Organization, 5686–5693
Yanci Zhang and Han Yu. 2022 · 2022
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Is Federated Learning a Practical PET Yet?
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, and Nicolas Papernot. 2023 · 2023
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FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information. In 44th IEEE Symposium on Security and Privacy, SP 2023, San Francisco, CA, USA, May 21-25, 2023 . IEEE, 1366–1383
Xiaoyu Cao, Jinyuan Jia, Zaixi Zhang, and Neil Zhenqiang Gong. 2023 · 2023
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The Federated Tumor Segmentation (FeTS) initiative
CBICA Center for Biomedical Image Computing & Analytics. 2020 · 2023
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The MELLODDY project
The European Union’s. 2019 · 2023
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