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Federated learning enables training high-utility models across several clients without directly sharing their private data.
Advances and open problems in federated learning
Peter Kairouz, H. B. McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary B. Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Salim Y. El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Oluwasanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, R. Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Xiaodong Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 1912
Earlier work this paper cites.
A survey of trust and reputation systems for online service provision
Audun Jøsang, Roslan Ismail, and Colin Boyd · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2015
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, and Tom Goldstein · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2017
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Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and X. Zhang · 2018
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How does batch normalization help optimization?
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry · 2018
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Leaf: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. B. McMahan, Virginia Smith, and Ameet S. Talwalkar · 2018
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Spectral signatures in backdoor attacks
Brandon Tran, Jerry Li, and Aleksander Madry · 2018
Earlier work this paper cites.
signSGD: Compressed optimisation for non-convex problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, and Animashree Anandkumar · 2018
Cited alongside, same era.
An alternative view: When does SGD escape local minima?
Bobby Kleinberg, Yuanzhi Li, and Yang Yuan · 2018
Cited alongside, same era.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y. Zhao · 2019
Cited alongside, same era.
Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H. B. McMahan · 2019
Cited alongside, same era.
signsgd with majority vote is communication efficient and fault tolerant
Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, and Anima Anandkumar · 2019
Cited alongside, same era.
Backdoor attacks against deep learning systems in the physical world
Emily Wenger, Josephine Passananti, Arjun Nitin Bhagoji, Yuanshun Yao, Haitao Zheng, and Ben Y. Zhao · 2021
Later among the works it cites.
Invisible backdoor attack with sample-specific triggers
Yuezun Li, Y. Li, Baoyuan Wu, Longkang Li, Ran He, and Siwei Lyu · 2021
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Flame: Taming backdoors in federated learning
Thien Duc Nguyen, Phillip Rieger, Huili Chen, Hossein Yalame, Helen Mollering, Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Shaza Zeitouni, Farinaz Koushanfar, Ahmad-Reza Sadeghi, and T. Schneider · 2021
Later among the works it cites.
Adversarial neuron pruning purifies backdoored deep models
Dongxian Wu and Yisen Wang · 2021
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A field guide to federated optimization
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H Brendan McMahan, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, et al · 2021
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Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, and Zhanxing Zhu · 2019
Cited alongside, same era.
Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 2019
Cited alongside, same era.
SGD converges to global minimum in deep learning via star-convex path
Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang, and Vahid Tarokh · 2019
Cited alongside, same era.
How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2020
Cited alongside, same era.
Attack of the tails: Yes, you really can backdoor federated learning
Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, and Dimitris Papailiopoulos · 2020
Cited alongside, same era.
The global landscape of neural networks: An overview
Ruoyu Sun, Dawei Li, Shiyu Liang, Tian Ding, and Rayadurgam Srikant · 2020
Cited alongside, same era.
Fltrust: Byzantine-robust federated learning via trust bootstrapping
Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong · 2020
Cited alongside, same era.
Defending against backdoors in federated learning with robust learning rate
Mustafa Safa Ozdayi, Murat Kantarcioglu, and Yulia R Gel · 2021
Later among the works it cites.
Can shape structure features improve model robustness under diverse adversarial settings?
Mingjie Sun, Zichao Li, Chaowei Xiao, Haonan Qiu, Bhavya Kailkhura, Mingyan Liu, and Bo Li · 2021
Later among the works it cites.
Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2021
Later among the works it cites.
Learning models with uniform performance via distributionally robust optimization
John C. Duchi and Hongseok Namkoong · 2021
Later among the works it cites.
Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning
Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, and Daniel Ramage · 2022
Closest in time.
Robust aggregation for federated learning
Krishna Pillutla, Sham M. Kakade, and Zaid Harchaoui · 2022
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Sparsefed: Mitigating model poisoning attacks in federated learning with sparsification
Ashwinee Panda, Saeed Mahloujifar, Arjun Nitin Bhagoji, Supriyo Chakraborty, and Prateek Mittal · 2022
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Byzantine-robust learning on heterogeneous datasets via bucketing
Sai Praneeth Karimireddy, Lie He, and Martin Jaggi · 2022
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FLIP: A provable defense framework for backdoor mitigation in federated learning
Kaiyuan Zhang, Guanhong Tao, Qiuling Xu, Siyuan Cheng, Shengwei An, Yingqi Liu, Shiwei Feng, Guangyu Shen, Pin-Yu Chen, Shiqing Ma, and Xiangyu Zhang · 2023
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Loss landscapes are all you need: Neural network generalization can be explained without the implicit bias of gradient descent
Ping yeh Chiang, Renkun Ni, David Yu Miller, Arpit Bansal, Jonas Geiping, Micah Goldblum, and Tom Goldstein · 2023
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