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Federated learning allows multiple parties to build machine learning models collaboratively without exposing data.
MixMatch: A Holistic Approach to Semi-Supervised Learning
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Private Federated Learning with Domain Adaptation
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Backdoor Attacks and Defenses in Feature-partitioned Collaborative Learning
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Learning and transferring mid-level image representations using convolutional neural networks. In In CVPR
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Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Domain Separation Networks
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan. 2016 · 2016
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Secure Linear Regression on Vertically Partitioned Datasets
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Federated Learning of Deep Networks using Model Averaging
H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas. 2016 · 2016
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Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar. 2018 · 2018
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Federated Machine Learning: Concept and Applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019a · 2019
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Inverting Gradients - How easy is it to break privacy in federated learning?. In Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 16937–16947
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller. 2020 · 2020
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A Secure Federated Transfer Learning Framework
Yang Liu, Yan Kang, Chaoping Xing, Tianjian Chen, and Qiang Yang. 2020a · 2020
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Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 2017 · 2017
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Temporal Ensembling for Semi-Supervised Learning.. In ICLR (Poster) . OpenReview.net
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SecureML: A System for Scalable Privacy-Preserving Machine Learning
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
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Attention is All you Need. In Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
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Data protection laws of the world: Full handbook
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General Data Protection Regulation
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Distributed learning of deep neural network over multiple agents
Otkrist Gupta and Ramesh Raskar. 2018 · 2018
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Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2020 · 2020
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Deep leakage from gradients
Ligeng Zhu and Song Han. 2020 · 2020
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SecureBoost: A Lossless Federated Learning Framework
Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, and Qiang Yang. 2021 · 2021
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Ensemble Attention Distillation for Privacy-Preserving Federated Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) . 15076–15086
Xuan Gong, Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, and Arun Innanje. 2021 · 2021
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Gradient Inversion with Generative Image Prior. In Advances in Neural Information Processing Systems , Vol. 34. Curran Associates, Inc
Jiwnoo Jeon, jaechang Kim, Kangwook Lee, Sewoong Oh, and Jungseul Ok. 2021 · 2021
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Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao. 2021 · 2021
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Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability
Yan Kang, Yang Liu, Yuezhou Wu, Guoqiang Ma, and Qiang Yang. 2021 · 2021
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Label Leakage and Protection in Two-party Split Learning
Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, and Chong Wang. 2021 · 2021
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FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection
Yang Liu, Tao Fan, Tianjian Chen, Qian Xu, and Qiang Yang. 2021b · 2021
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