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Federated learning has emerged as a popular paradigm for collaboratively training a model from data distributed among a set of clients.
Reorthogonalization and stable algorithms for updating the gram-schmidt qr factorization
James W Daniel, Walter Bill Gragg, Linda Kaufman, and Gilbert W Stewart · 1976
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Diffie-hellman key distribution extended to group communication
Michael Steiner, Gene Tsudik, and Michael Waidner · 1996
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Elements of information theory
Thomas M Cover · 1999
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Secure computation on floating point numbers
Mehrdad Aliasgari, Marina Blanton, Yihua Zhang, and Aaron Steele · 2013
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Intel sgx explained
Victor Costan and Srinivas Devadas · 2016
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Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, Rachid Guerraoui, Julien Stainer, et al · 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
Earlier work this paper cites.
Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Yudong Chen, Lili Su, and Jiaming Xu · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Cited alongside, same era.
The hidden vulnerability of distributed learning in byzantium
El Mahdi El Mhamdi, Rachid Guerraoui, and Sébastien Rouault · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett · 2018
Cited alongside, same era.
Analyzing federated learning through an adversarial lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo · 2019
Cited alongside, same era.
How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2020
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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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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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Secure multiparty computations in floating-point arithmetic
Chuan Guo, Awni Hannun, Brian Knott, Laurens van der Maaten, Mark Tygert, and Ruiyu Zhu · 2020
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Justinian’s gaavernor: Robust distributed learning with gradient aggregation agent
Xudong Pan, Mi Zhang, Duocai Wu, Qifan Xiao, Shouling Ji, and Zhemin Yang · 2020
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A survey on federated learning systems: vision, hype and reality for data privacy and protection
Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, and Bingsheng He · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
Cited alongside, same era.
Byzantine-resilient stochastic gradient descent for distributed learning: A lipschitz-inspired coordinate-wise median approach
Haibo Yang, Xin Zhang, Minghong Fang, and Jia Liu · 2019
Cited alongside, same era.
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
Cited alongside, same era.
Byzantine-robust decentralized stochastic optimization
Jie Peng and Qing Ling · 2020
Later among the works it cites.
A scalable approach for privacy-preserving collaborative machine learning
Jinhyun So, Basak Guler, and Salman Avestimehr · 2020
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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 Gündüz · 2021
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