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Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information.
Safeguarding cryptographic keys
G.R. Blakley · 1979
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How to share a secret
Adi Shamir · 1979
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Public-key cryptosystems based on composite degree residuosity classes
Pascal Paillier · 1999
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A new metric for probability distributions
Dominik Maria Endres and Johannes E Schindelin · 2003
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A fully homomorphic encryption scheme
Craig Gentry · 2009
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Privacy against statistical inference
Flávio du Pin Calmon and Nadia Fawaz · 2012
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Privacy-utility tradeoff under statistical uncertainty
Ali Makhdoumi and Nadia Fawaz · 2013
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Utility-privacy tradeoffs in databases: An information-theoretic approach
Lalitha Sankar, S Raj Rajagopalan, and H Vincent Poor · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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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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Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečný, H. Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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On the relation between identifiability, differential privacy, and mutual-information privacy
Weina Wang, Lei Ying, and Junshan Zhang · 2016
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Privacy-preserving deep learning via additively homomorphic encryption
Yoshinori Aono, Takuya Hayashi, Lihua Wang, Shiho Moriai, et al · 2017
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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An estimation-theoretic view of privacy
Hao Wang and Flavio P Calmon · 2017
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Distributed learning of deep neural network over multiple agents
Otkrist Gupta and Ramesh Raskar · 2018
Cited alongside, same era.
Federated learning for mobile keyboard prediction, 2018
Andrew Hard, Chloé M Kiddon, Daniel Ramage, Francoise Beaufays, Hubert Eichner, Kanishka Rao, Rajiv Mathews, and Sean Augenstein · 2018
Cited alongside, same era.
Optimal utility-privacy trade-off with total variation distance as a privacy measure
Borzoo Rassouli and Deniz Gündüz · 2019
Cited alongside, same era.
A hybrid approach to privacy-preserving federated learning
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou · 2019
Cited alongside, same era.
Adaptive communication strategies to achieve the best error-runtime trade-off in local-update sgd
Ldp-fed: Federated learning with local differential privacy
Stacey Truex, Ling Liu, Ka-Ho Chow, Mehmet Emre Gursoy, and Wenqi Wei · 2020
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Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning
Chengliang Zhang, Suyi Li, Junzhe Xia, Wei Wang, Feng Yan, and Yang Liu · 2020
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idlg: Improved deep leakage from gradients
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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Vertical federated learning for higher-order factorization machines
Kyohei Atarashi and Masakazu Ishihata · 2021
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Secureboost: A lossless federated learning framework
Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, Dimitrios Papadopoulos, and Qiang Yang · 2021
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Jianyu Wang and Gauri Joshi · 2019
Cited alongside, same era.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
Cited alongside, same era.
Pefl: A privacy-enhanced federated learning scheme for big data analytics
Jiale Zhang, Bing Chen, Shui Yu, and Hai Deng · 2019
Cited alongside, same era.
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
Cited alongside, same era.
Breaking the communication-privacy-accuracy trilemma
Wei-Ning Chen, Peter Kairouz, and Ayfer Ozgur · 2020
Cited alongside, same era.
Constraining variational inference with geometric jensen-shannon divergence
Jacob Deasy, Nikola Simidjievski, and Pietro Lió · 2020
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.
Haokun Fang and Quan Qian · 2021
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Federated deep learning with bayesian privacy
Hanlin Gu, Lixin Fan, Bowen Li, Yan Kang, Yuan Yao, and Qiang Yang · 2021
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Fast federated learning by balancing communication trade-offs
Milad Khademi Nori, Sangseok Yun, and Il-Min Kim · 2021
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Swarm learning for decentralized and confidential clinical machine learning
Stefanie Warnat-Herresthal, Hartmut Schultze, Krishnaprasad Lingadahalli Shastry, Sathyanarayanan Manamohan, Saikat Mukherjee, Vishesh Garg, Ravi Sarveswara, Kristian Händler, Peter Pickkers, N Ahmad Aziz, et al · 2021
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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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Federated learning for healthcare: Systematic review and architecture proposal
Rodolfo Stoffel Antunes, Cristiano André da Costa, Arne Küderle, Imrana Abdullahi Yari, and Björn Eskofier · 2022
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Label inference attacks against vertical federated learning
Chong Fu, Xuhong Zhang, Shouling Ji, Jinyin Chen, Jingzheng Wu, Shanqing Guo, Jun Zhou, Alex X. Liu, and Ting Wang · 2022
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Privacy-preserving federated adversarial domain adaptation over feature groups for interpretability
Yan Kang, Yuanqin He, Jiahuan Luo, Tao Fan, Yang Liu, and Qiang Yang · 2022
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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 · 2022
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Yang Liu, Yan Kang, Tianyuan Zou, Yanhong Pu, Yuanqin He, Xiaozhou Ye, Ye Ouyang, Ya-Qin Zhang, and Qiang Yang · 2022
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No free lunch theorem for security and utility in federated learning
Xiaojin Zhang, Hanlin Gu, Lixin Fan, Kai Chen, and Qiang Yang · 2022
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Defending batch-level label inference and replacement attacks in vertical federated learning
T. Zou, Y. Liu, Y. Kang, W. Liu, Y. He, Z. Yi, Q. Yang, and Y. Zhang · 2022
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