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Federated learning (FL) aims to protect data privacy by enabling clients to build machine learning models collaboratively without sharing their private data.
Split learning for collaborative deep learning in healthcare
Poirot, M. G.; Vepakomma, P.; Chang, K.; Kalpathy-Cramer, J.; Gupta, R.; and Raskar, R. 2019 · 1912
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idlg: Improved deep leakage from gradients
Zhao, B.; Mopuri, K. R.; and Bilen, H. 2020 · 2001
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Fedgan: Federated generative adversarial networks for distributed data
Rasouli, M.; Sun, T.; and Rajagopal, R. 2020 · 2006
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A fully homomorphic encryption scheme , volume 20
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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The algorithmic foundations of differential privacy
Dwork, C.; Roth, A.; et al. 2014 · 2014
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Distilling the Knowledge in a Neural Network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2014
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Conditional generative adversarial nets
Mirza, M.; and Osindero, S. 2014 · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M.; Jha, S.; and Ristenpart, T. 2015 · 2015
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LeNet-5, convolutional neural networks
LeCun, Y.; et al. 2015 · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A.; Metz, L.; and Chintala, S. 2015 · 2015
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Privacy-preserving deep learning via additively homomorphic encryption
Aono, Y.; Hayashi, T.; Wang, L.; Moriai, S.; et al. 2017 · 2017
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Deep models under the GAN: information leakage from collaborative deep learning
Hitaj, B.; Ateniese, G.; and Perez-Cruz, F. 2017 · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H.; Rasul, K.; and Vollgraf, R. 2017 · 2017
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Distributed learning of deep neural network over multiple agents
Gupta, O.; and Raskar, R. 2018 · 2018
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Inverting Gradients - How easy is it to break privacy in federated learning?
Geiping, J.; Bauermeister, H.; Dröge, H.; and Moeller, M. 2020 · 2020
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Federated Optimization in Heterogeneous Networks
Li, T.; Sahu, A. K.; Zaheer, M.; Sanjabi, M.; Talwalkar, A.; and Smith, V. 2020 · 2020
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Ensemble Distillation for Robust Model Fusion in Federated Learning
Lin, T.; Kong, L.; Stich, S. U.; and Jaggi, M. 2020 · 2020
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Deep leakage from gradients
Zhu, L.; and Han, S. 2020 · 2020
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Federated Deep Learning with Bayesian Privacy
Gu, H.; Fan, L.; Li, B.; Kang, Y.; Yao, Y.; and Yang, Q. 2021 · 2021
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Federated learning on non-iid data silos: An experimental study
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Vepakomma, P.; Gupta, O.; Swedish, T.; and Raskar, R. 2018 · 2018
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Md-gan: Multi-discriminator generative adversarial networks for distributed datasets
Hardy, C.; Le Merrer, E.; and Sericola, B. 2019 · 2019
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Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
Mao, Q.; Lee, H.-Y.; Tseng, H.-Y.; Ma, S.; and Yang, M.-H. 2019 · 2019
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Moment matching for multi-source domain adaptation
Peng, X.; Bai, Q.; Xia, X.; Huang, Z.; Saenko, K.; and Wang, B. 2019 · 2019
Cited alongside, same era.
Characterizing and Avoiding Negative Transfer
Wang, Z.; Dai, Z.; Póczos, B.; and Carbonell, J. 2019a · 2019
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Beyond inferring class representatives: User-level privacy leakage from federated learning
Wang, Z.; Song, M.; Zhang, Z.; Song, Y.; Wang, Q.; and Qi, H. 2019b · 2019
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Li, Q.; Diao, Y.; Chen, Q.; and He, B. 2021 · 2021
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Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets
Mugunthan, V.; Gokul, V.; Kagal, L.; and Dubnov, S. 2021 · 2021
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GRNN: Generative Regression Neural Network–A Data Leakage Attack for Federated Learning
Ren, H.; Deng, J.; and Xie, X. 2021 · 2021
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Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data
Zhao, Z.; Birke, R.; Kunar, A.; and Chen, L. Y. 2021 · 2021
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Data-Free Knowledge Distillation for Heterogeneous Federated Learning
Zhu, Z.; Hong, J.; and Zhou, J. 2021 · 2021
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Geodesic flow kernel for unsupervised domain adaptation
Gong, B.; Shi, Y.; Sha, F.; and Grauman, K. 2012 · 2073
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