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Federated learning (FL) typically relies on synchronous training, which is slow due to stragglers.
The theory of error correcting codes
Florence Jessie MacWilliams and Neil James Alexander Sloane · 1977
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The MNIST database of handwritten digits
Yann LeCun · 1998
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 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
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Intel sgx explained
Victor Costan and Srinivas Devadas · 2016
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Qsgd: Communication-efficient sgd via gradient quantization and encoding
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2017
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Federated learning with autotuned communication-efficient secure aggregation
Keith Bonawitz, Fariborz Salehi, Jakub Konečnỳ, Brendan McMahan, and Marco Gruteser · 2019
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
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Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2019
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Asynchronous federated optimization
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 2019
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Secure single-server aggregation with (poly) logarithmic overhead
James Henry Bell, Kallista A Bonawitz, Adrià Gascón, Tancrède Lepoint, and Mariana Raykova · 2020
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Zheng Chai, Yujing Chen, Liang Zhao, Yue Cheng, and Huzefa Rangwala · 2020
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Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2020
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Ldp-fed: Federated learning with local differential privacy
Stacey Truex, Ling Liu, Ka-Ho Chow, Mehmet Emre Gursoy, and Wenqi Wei · 2020
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Marten van Dijk, Nhuong V Nguyen, Toan N Nguyen, Lam M Nguyen, Quoc Tran-Dinh, and Phuong Ha Nguyen · 2020
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Deep leakage from gradients
Ligeng Zhu and Song Han · 2020
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Privacy-preserving asynchronous vertical federated learning algorithms for multiparty collaborative learning
Bin Gu, An Xu, Zhouyuan Huo, Cheng Deng, and Heng Huang · 2021
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Asynchronous online federated learning for edge devices with non-iid data
Yujing Chen, Yue Ning, Martin Slawski, and Huzefa Rangwala · 2020
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Secure aggregation with heterogeneous quantization in federated learning
Ahmed Roushdy Elkordy and A Salman Avestimehr · 2020
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Inverting gradients–how easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller · 2020
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Fastsecagg: Scalable secure aggregation for privacy-preserving federated learning
Swanand Kadhe, Nived Rajaraman, O Ozan Koyluoglu, and Kannan Ramchandran · 2020
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Sself: Robust federated learning against stragglers and adversaries
Jungwuk Park, Dong-Jun Han, Minseok Choi, and Jaekyun Moon · 2020
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John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Michael Rabbat, Mani Malek Esmaeili, and Dzmitry Huba · 2021
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Securing secure aggregation: Mitigating multi-round privacy leakage in federated learning
Jinhyun So, Ramy E Ali, Basak Guler, Jiantao Jiao, and Salman Avestimehr · 2021
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Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning
Jinhyun So, Başak Güler, and A Salman Avestimehr · 2021
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LightSecAgg: Rethinking secure aggregation in federated learning
Chien-Sheng Yang, Jinhyun So, Chaoyang He, Songze Li, Qian Yu, and Salman Avestimehr · 2021
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Information theoretic secure aggregation with user dropouts
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