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Federated learning (FL) has been widely deployed to enable machine learning training on sensitive data across distributed devices.
Predicting Parameters in Deep Learning
Misha Denil, Babak Shakibi, Laurent Dinh, Marc’Aurelio Ranzato, and Nando De Freitas · 2013
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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An Exploration of Parameter Redundancy in Deep Networks With Circulant Projections
Yu Cheng, Felix X Yu, Rogerio S Feris, Sanjiv Kumar, Alok Choudhary, and Shi-Fu Chang · 2015
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Federated Learning: Strategies for Improving Communication Efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Earlier work this paper cites.
Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
Earlier work this paper cites.
Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
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BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 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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signSGD With Majority Vote Is Communication Efficient and Fault Tolerant
Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, and Anima Anandkumar · 2018
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Leaf: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
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The hidden vulnerability of distributed learning in byzantium
Mahdi El El Mhamdi, Rachid Guerraoui, and Sébastien Rouault · 2018
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Effnet: An efficient structure for convolutional neural networks
Ido Freeman, Lutz Roese-Koerner, and Anton Kummert · 2018
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Fine-pruning: Defending against backdooring attacks on deep neural networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2018
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Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett · 2018
Earlier work this paper cites.
A little is enough: Circumventing defenses for distributed learning
Gilad Baruch, Moran Baruch, and Yoav Goldberg · 2019
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Analyzing Federated Learning Through an Adversarial Lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo · 2019
Cited alongside, same era.
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Jonathan Frankle and Michael Carbin · 2019
Cited alongside, same era.
Abs: Scanning neural networks for back-doors by artificial brain stimulation
Yingqi Liu, Wen-Chuan Lee, Guanhong Tao, Shiqing Ma, Yousra Aafer, and Xiangyu Zhang · 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.
Bit-flip attack: Crushing neural network with progressive bit search
Adnan Siraj Rakin, Zhezhi He, and Deliang Fan · 2019
Cited alongside, same era.
Deep feature space trojan attack of neural networks by controlled detoxification
Siyuan Cheng, Yingqi Liu, Shiqing Ma, and Xiangyu Zhang · 2021
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Lora: Low-rank Adaptation of Large Language Models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Deeppayload: Black-box backdoor attack on deep learning models through neural payload injection
Yuanchun Li, Jiayi Hua, Haoyu Wang, Chunyang Chen, and Yunxin Liu · 2021
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Flame: Taming backdoors in federated learning
Thien Duc Nguyen, Phillip Rieger, Huili Chen, Hossein Yalame, Helen Möllering, Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Shaza Zeitouni, et al · 2021
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Defending against backdoors in federated learning with robust learning rate
Mustafa Safa Ozdayi, Murat Kantarcioglu, and Yulia R Gel · 2021
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Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan · 2019
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.
DBA: Distributed Backdoor Attacks Against Federated Learning
Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li · 2019
Cited alongside, same era.
Latent backdoor attacks on deep neural networks
Yuanshun Yao, Huiying Li, Haitao Zheng, and Ben Y Zhao · 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.
Targeted attack against deep neural networks via flipping limited weight bits
Jiawang Bai, Baoyuan Wu, Yong Zhang, Yiming Li, Zhifeng Li, and Shu-Tao Xia · 2020
Cited alongside, same era.
Local model poisoning attacks to byzantine-robust federated learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong · 2020
Cited alongside, same era.
T-bfa: Targeted bit-flip adversarial weight attack
Adnan Siraj Rakin, Zhezhi He, Jingtao Li, Fan Yao, Chaitali Chakrabarti, and Deliang Fan · 2021
Later among the works it cites.
Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning
Virat Shejwalkar and Amir Houmansadr · 2021
Later among the works it cites.
Backdoor pre-trained models can transfer to all
Lujia Shen, Shouling Ji, Xuhong Zhang, Jinfeng Li, Jing Chen, Jie Shi, Chengfang Fang, Jianwei Yin, and Ting Wang · 2021
Later among the works it cites.
On the geometry of generalization and memorization in deep neural networks
Cory Stephenson, Suchismita Padhy, Abhinav Ganesh, Yue Hui, Hanlin Tang, and SueYeon Chung · 2021
Later among the works it cites.
Adversarial neuron pruning purifies backdoored deep models
Dongxian Wu and Yisen Wang · 2021
Later among the works it cites.
Fiba: Frequency-injection based backdoor attack in medical image analysis
Yu Feng, Benteng Ma, Jing Zhang, Shanshan Zhao, Yong Xia, and Dacheng Tao · 2022
Later among the works it cites.
Towards practical deployment-stage backdoor attack on deep neural networks
Xiangyu Qi, Tinghao Xie, Ruizhe Pan, Jifeng Zhu, Yong Yang, and Kai Bu · 2022
Later among the works it cites.
Deepsight: Mitigating backdoor attacks in federated learning through deep model inspection
Phillip Rieger, Thien Duc Nguyen, Markus Miettinen, and Ahmad-Reza Sadeghi · 2022
Later among the works it cites.
Dynamic backdoor attacks against machine learning models
Ahmed Salem, Rui Wen, Michael Backes, Shiqing Ma, and Yang Zhang · 2022
Later among the works it cites.
Flare: defending federated learning against model poisoning attacks via latent space representations
Ning Wang, Yang Xiao, Yimin Chen, Yang Hu, Wenjing Lou, and Y Thomas Hou · 2022
Later among the works it cites.
Perdoor: Persistent backdoors in federated learning using adversarial perturbations
Manaar Alam, Esha Sarkar, and Michail Maniatakos · 2023
Closest in time.
3dfed: Adaptive and extensible framework for covert backdoor attack in federated learning
Haoyang Li, Qingqing Ye, Haibo Hu, Jin Li, Leixia Wang, Chengfang Fang, and Jie Shi · 2023
Closest in time.
Can neural network memorization be localized?
Pratyush Maini, Michael C Mozer, Hanie Sedghi, Zachary C Lipton, J Zico Kolter, and Chiyuan Zhang · 2023
Closest in time.
Towards Building the Federated GPT: Federated Instruction Tuning, 2023
Jianyi Zhang, Saeed Vahidian, Martin Kuo, Chunyuan Li, Ruiyi Zhang, Guoyin Wang, and Yiran Chen · 2023
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