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Most real-world data are scattered across different companies or government organizations, and cannot be easily integrated under data privacy and related regulations such as the European Union's General Data Protection Regulation (GDPR) and China' Cyber Security Law.
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Differentially Private Federated Learning: A Client Level Perspective
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Learning Differentially Private Recurrent Language Models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2017 · 2017
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LoAdaBoost:Loss-Based AdaBoost Federated Machine Learning on medical Data
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Secure logistic regression based on homomorphic encryption: Design and evaluation
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Privacy-Preserving Deep Learning via Additively Homomorphic Encryption
Trieu Phong Le, Yoshinori Aono, Takuya Hayashi, Lihua Wang, and Shiho Moriai. 2018 · 2018
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Entity Resolution and Federated Learning get a Federated Resolution
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Federated Multi-Task Learning
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LEAF: A Benchmark for Federated Settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar. 2018 · 2018
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Federated Meta-Learning for Recommendation
Fei Chen, Zhenhua Dong, Zhenguo Li, and Xiuqiang He. 2018 · 2018
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A data-driven statistical model for predicting the critical temperature of a superconductor
Kam Hamidieh. 2018 · 2018
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Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 2017a
Cited in the paper.
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 2017b
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Privacy-Preserving Naive Bayes Classification Using Fully Homomorphic Encryption. In Neural Information Processing , Long Cheng, Andrew Chi Sing Leung, and Seiichi Ozawa (Eds.). Springer International Publishing, Cham, 349–358
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Blaise Barney. 2019 · 2019
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P3652.1 - Guide for Architectural Framework and Application of Federated Machine Learning
Federated Machine Learning Working Group. 2019 · 2019
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A comparative analysis of speech signal processing algorithms for Parkinsonś disease classification and the use of the tunable Q-factor wavelet transform
C Okan Sakar, Gorkem Serbes, Aysegul Gunduz, Hunkar C Tunc, Hatice Nizam, Betul Erdogdu Sakar, Melih Tutuncu, Tarkan Aydin, M Erdem Isenkul, and Hulya Apaydin. 2019 · 2019
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Federated Machine Learning: Concept and Applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019 · 2019
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