Fetching the paper…
Reading the bibliography…
In federated learning systems, clients are autonomous in that their behaviors are not fully governed by the server.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
Earlier work this paper cites.
Anomaly detection: A survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar · 2009
Earlier work this paper cites.
Anomaly detection using autoencoders with nonlinear dimensionality reduction
Mayu Sakurada and Takehisa Yairi · 2014
Earlier work this paper cites.
Linear dimensionality reduction: Survey, insights, and generalizations
John P. Cunningham and Zoubin Ghahramani · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Oblivious multi-party machine learning on trusted processors
Olga Ohrimenko, Felix Schuster, Cédric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, and Manuel Costa · 2016
Earlier work this paper cites.
Auror: Defending against poisoning attacks in collaborative deep learning systems
Shiqi Shen, Shruti Tople, and Prateek Saxena · 2016
Earlier work this paper cites.
Weight features for predicting future model performance of deep neural networks
Yasunori Yamada and Tetsuro Morimura · 2016
Earlier work this paper cites.
Federated learning of deep networks using model averaging
H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Aguera y Arcas · 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.
Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Yudong Chen, Lili Su, and Jiaming Xu · 2017
Cited alongside, same era.
Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
Cited alongside, same era.
Federated learning: Strategies for improving communication efficiency
Jakub Konecný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2017
Cited alongside, same era.
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar · 2018
Cited alongside, same era.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
Closest in time.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
Closest in time.
Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning
Hao Yu, Sen Yang, and Shenghuo Zhu · 2019
Closest in time.
Federated optimization for heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2019
Closest in time.
Distributed training with heterogeneous data: Bridging median- and mean-based algorithms
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Micah J. Sheller, G. Anthony Reina, Brandon Edwards, Jason Martin, and Spyridon Bakas · 2018
Cited alongside, same era.
Differentially private federated learning: A client level perspective
Robin C. Geyer, Tassilo Klein, and Moin Nabi · 2018
Cited alongside, same era.
Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett · 2018
Cited alongside, same era.
Liping Li, Wei Xu, Tianyi Chen, Georgios B. Giannakis, and Qing Ling · 2018
Cited alongside, same era.
Leaf: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
Cited alongside, same era.
Xiangyi Chen, Tiancong Chen, Haoran Sun, Zhiwei Steven Wu, and Mingyi Hong · 2019
Closest in time.
Privacy-preserving deep learning via weight transmission
L. T. Phong and T. T. Phuong · 2019
Closest in time.
Incentive design for efficient federated learning in mobile networks: A contract theory approach
Jiawen Kang, Zehui Xiong, Dusit Niyato, Han Yu, Ying-Chang Liang, and Dong In Kim · 2019
Closest in time.
Analyzing federated learning through an adversarial lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin B. Calo · 2019
Closest in time.
How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2019
Closest in time.
Liping Li, Wei Xu, Tianyi Chen, Georgios B Giannakis, and Qing Ling · 2019
Closest in time.