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Federated learning (FL) emerged as a promising learning paradigm to enable a multitude of participants to construct a joint ML model without exposing their private training data.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
Earlier work this paper cites.
Deep models under the gan: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Pérez-Cruz · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Earlier work this paper cites.
Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Cited alongside, same era.
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, et al · 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.
Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2019
Cited alongside, same era.
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
Cited alongside, same era.
Privacy and robustness in federated learning: Attacks and defenses
Lingjuan Lyu, Han Yu, Xingjun Ma, Lichao Sun, Jun Zhao, Qiang Yang, and Philip S Yu · 2020
Later among the works it cites.
Threats to federated learning: A survey
Lingjuan Lyu, Han Yu, and Qiang Yang · 2020
Later among the works it cites.
Information leakage in embedding models
Congzheng Song and Ananth Raghunathan · 2020
Later among the works it cites.
idlg: Improved deep leakage from gradients
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
Later among the works it cites.
Personalized cross-silo federated learning on non-iid data
Yutao Huang, Lingyang Chu, Zirui Zhou, Lanjun Wang, Jiangchuan Liu, Jian Pei, and Yong Zhang · 2021
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