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Federated learning is an established method for training machine learning models without sharing training data.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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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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Conditional image generation with pixelcnn decoders
Aäron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Koray Kavukcuoglu, Oriol Vinyals, and Alex Graves · 2016
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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Privacy-preserving deep learning: Revisited and enhanced
Le Trieu Phong, Yoshinori Aono, Takuya Hayashi, Lihua Wang, and Shiho Moriai · 2017
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Glow: Generative flow with invertible 1x1 convolutions
Diederik P. Kingma and Prafulla Dhariwal · 2018
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Autoaugment: Learning augmentation strategies from data
Ekin D. Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, and Quoc V. Le · 2019
Cited alongside, same era.
Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
Cited alongside, same era.
Inverting gradients - how easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller · 2020
Cited alongside, same era.
A framework for evaluating client privacy leakages in federated learning
Wenqi Wei, Ling Liu, Margaret Loper, Ka Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu · 2020
Cited alongside, same era.
idlg: Improved deep leakage from gradients, 2020
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
Cited alongside, same era.
Privacy-preserving collaborative learning with automatic transformation search
Digestive neural networks: A novel defense strategy against inference attacks in federated learning
Hongkyu Lee, Jeehyeong Kim, Seyoung Ahn, Rasheed Hussain, Sunghyun Cho, and Junggab Son · 2021
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Quantifying information leakage from gradients
Fan Mo, Anastasia Borovykh, Mohammad Malekzadeh, Hamed Haddadi, and Soteris Demetriou · 2021
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Precode - a generic model extension to prevent deep gradient leakage, 2021
Daniel Scheliga, Patrick Mäder, and Marco Seeland · 2021
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Soteria: Provable defense against privacy leakage in federated learning from representation perspective
Jingwei Sun, Ang Li, Binghui Wang, Huanrui Yang, Hai Li, and Yiran Chen · 2021
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Gradient-leakage resilient federated learning
Wenqi Wei, Ling Liu, Yanzhao Wu, Gong Su, and Arun Iyengar · 2021
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Wei Gao, Shangwei Guo, Tianwei Zhang, Han Qiu, Yonggang Wen, and Yang Liu · 2021
Cited alongside, same era.
Towards general deep leakage in federated learning
Jiahui Geng, Yongli Mou, Feifei Li, Qing Li, Oya Beyan, Stefan Decker, and Chunming Rong · 2021
Cited alongside, same era.
Gradient inversion with generative image prior
Jinwoo Jeon, Jaechang Kim, Kangwook Lee, Sewoong Oh, and Jungseul Ok · 2021
Cited alongside, same era.
Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečný, H. Brendan McMahan, Daniel Ramage, and Peter Richtárik
Cited in the paper.
Federated learning: Strategies for improving communication efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon
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See through gradients: Image batch recovery via gradinversion
Hongxu Yin, Arun Mallya, Arash Vahdat, Jose M. Alvarez, Jan Kautz, and Pavlo Molchanov · 2021
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Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2021
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