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Collaborative learning has gained great popularity due to its benefit of data privacy protection: participants can jointly train a Deep Learning model without sharing their training sets.
On the limited memory BFGS method for large scale optimization
Dong C Liu and Jorge Nocedal · 1989
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Radford M Neal · 2001
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Evolutionary particle filter: Re-sampling from the genetic algorithm perspective
Ngai Ming Kwok, Gu Fang, and Weizhen Zhou · 2005
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Image quality metrics: PSNR vs. SSIM
Alain Hore and Djemel Ziou · 2010
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Deep models under the GAN: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Federated learning of predictive models from federated electronic health records
Theodora S Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Ch Paschalidis, and Wei Shi · 2018
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Contextual augmentation: Data augmentation by words with paradigmatic relations
Sosuke Kobayashi · 2018
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On the discrepancy between the theoretical analysis and practical implementations of compressed communication for distributed deep learning
Dutta Aritra, Houcine Bergou El, M. Abdelmoniem Ahmed, Ho Chen-Yu, Narayan Sahu Atal, Canini Marco, and Kalnis Panos · 2019
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Autoaugment: Learning augmentation strategies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
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Efficient and privacy-enhanced federated learning for industrial artificial intelligence
Meng Hao, Hongwei Li, Xizhao Luo, Guowen Xu, Haomiao Yang, and Sen Liu · 2019
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Model inversion attacks against collaborative inference
Zecheng He, Tianwei Zhang, and Ruby B Lee · 2019
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Certified robustness to adversarial examples with differential privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2019
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Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
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Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Differentially private decentralized learning
Shangwei Guo, Tianwei Zhang, Tao Xiang, and Yang Liu · 2020
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Towards byzantine-resilient learning in decentralized systems
Shangwei Guo, Tianwei Zhang, Xiaofei Xie, Lei Ma, Tao Xiang, and Yang Liu · 2020
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Attacking and protecting data privacy in edge-cloud collaborative inference systems
Zecheng He, Tianwei Zhang, and Ruby B Lee · 2020
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Neural architecture search without training
Mellor Joseph, Turner Jack, Storkey Amos, and J. Crowley Elliot · 2020
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Federated learning for wireless communications: Motivation, opportunities, and challenges
Solmaz Niknam, Harpreet S Dhillon, and Jeffrey H Reed · 2020
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Milad Nasr, Reza Shokri, and Amir Houmansadr · 2019
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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
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Eda: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou · 2019
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A survey on neural architecture search
Martin Wistuba, Ambrish Rawat, and Tejaswini Pedapati · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
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Rethinking privacy preserving deep learning: How to evaluate and thwart privacy attacks
Lixin Fan, Kam Woh Ng, Ce Ju, Tianyu Zhang, Chang Liu, Chee Seng Chan, and Qiang Yang · 2020
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Scalable differential privacy with certified robustness in adversarial learning
Hai Phan, My T Thai, Han Hu, Ruoming Jin, Tong Sun, and Dejing Dou · 2020
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Fencebox: A platform for defeating adversarial examples with data augmentation techniques
Han Qiu, Yi Zeng, Tianwei Zhang, Yong Jiang, and Meikang Qiu · 2020
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Mitigating advanced adversarial attacks with more advanced gradient obfuscation techniques
Han Qiu, Yi Zeng, Qinkai Zheng, Tianwei Zhang, Meikang Qiu, and Gerard Memmi · 2020
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Towards secure and efficient deep learning inference in dependable iot systems
Han Qiu, Qinkai Zheng, Tianwei Zhang, Meikang Qiu, Gerard Memmi, and Jialiang Lu · 2020
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A framework for evaluating gradient leakage attacks in federated learning
Wenqi Wei, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu · 2020
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iDLG: Improved deep leakage from gradients
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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PrivateDL: Privacy-preserving collaborative deep learning against leakage from gradient sharing
Qi Zhao, Chuan Zhao, Shujie Cui, Shan Jing, and Zhenxiang Chen · 2020
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Deepsweep: An evaluation framework for mitigating dnn backdoor attacks using data augmentation
Yi Zeng, Han Qiu, Shangwei Guo, Tianwei Zhang, Meikang Qiu, and Bhavani Thuraisingham · 2021
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