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Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation owing to its strong potential in capturing underlying data statistics while preserving data privacy.
Consistency regularization for generative adversarial networks
Han Zhang, Zizhao Zhang, Augustus Odena, and Honglak Lee · 1910
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Differential privacy
Cynthia Dwork · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Federated learning: Strategies for improving communication efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2016
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, and Vitaly Shmatikov · 2016
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LOGAN: evaluating privacy leakage of generative models using generative adversarial networks
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2017
Cited alongside, same era.
Deep models under the gan: Information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Eunjeong Jeong, Seungeun Oh, Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim · 2018
Cited alongside, same era.
Hybrid-fl for wireless networks: Cooperative learning mechanism using non-iid data
Naoya Yoshida, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto, and Ryo Yonetani · 2020
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Compressive differentiallyprivate federated learning through universal vector quantization
Saba Amiri, Adam Belloum, Sander Klous, and Leon Gommans · 2021
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Towards fair federated learning with zero-shot data augmentation
Weituo Hao, Mostafa El-Khamy, Jungwon Lee, Jianyi Zhang, Kevin J Liang, Changyou Chen, and Lawrence Carin · 2021
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No fear of heterogeneity: Classifier calibration for federated learning with non-iid data
Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang, and Jiashi Feng · 2021
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Fedcg: Leverage conditional gan for protecting privacy and maintaining competitive performance in federated learning
Yuezhou Wu, Yan Kang, Jiahuan Luo, Yuanqin He, and Qiang Yang · 2021
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Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
Cited alongside, same era.
Ganobfuscator: Mitigating information leakage under gan via differential privacy
Chugui Xu, Ju Ren, Deyu Zhang, Yaoxue Zhang, Zhan Qin, and Kui Ren · 2019
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.
SCAFFOLD: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Cited alongside, same era.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Cited alongside, same era.
Privacy and robustness in federated learning: Attacks and defenses
L. Lyu, Han Yu, Xingjun Ma, Lichao Sun, Jun Zhao, Qiang Yang, and Philip S. Yu · 2020
Cited alongside, same era.
Private fl-gan: Differential privacy synthetic data generation based on federated learning
Bangzhou Xin, Wei Yang, Yangyang Geng, Sheng Chen, Shaowei Wang, and Liusheng Huang · 2020
Cited alongside, same era.
Analysis and mitigations of reverse engineering attacks on local feature descriptors
Deeksha Dangwal, Vincent T. Lee, Hyo Jin Kim, Tianwei Shen, Meghan Cowan, Rajvi Shah, Caroline Trippel, Brandon Reagen, Timothy Sherwood, Vasileios Balntas, Armin Alaghi, and Eddy Ilg
Cited in the paper.
Federated synthetic data generation with differential privacy
Bangzhou Xin, Yangyang Geng, Teng Hu, Sheng Chen, Wei Yang, Shaowei Wang, and Liusheng Huang · 2021
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Fedmix: Approximation of mixup under mean augmented federated learning
Tehrim Yoon, Sumin Shin, Sung Ju Hwang, and Eunho Yang · 2021
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Perfed-gan: Personalized federated learning via generative adversarial networks
Xingjian Cao, Gang Sun, Hongfang Yu, and Mohsen Guizani · 2022
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Ifl-gan: Improved federated learning generative adversarial network with maximum mean discrepancy model aggregation
Wei Li, Jinlin Chen, Zhenyu Wang, Zhidong Shen, Chao Ma, and Xiaohui Cui · 2022
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Rethinking data heterogeneity in federated learning: Introducing a new notion and standard benchmarks
Saeed Vahidian, Mahdi Morafah, Chen Chen, Mubarak Shah, and Bill Lin · 2022
Later among the works it cites.