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Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions.
Boyi Liu, Lujia Wang, Ming Liu, and Chengzhong Xu · 1901
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Asynchronous federated optimization
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 1903
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Incentive design for efficient federated learning in mobile networks: A contract theory approach
Jiawen Kang, Zehui Xiong, Dusit Niyato, Han Yu, Ying-Chang Liang, and Dong In Kim · 1905
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Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, and Virginia Smith · 1905
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Edge-assisted hierarchical federated learning with non-iid data
Lumin Liu, Jun Zhang, SH Song, and Khaled B Letaief · 1905
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Yang Liu, Yingting Liu, Zhijie Liu, Junbo Zhang, Chuishi Meng, and Yu Zheng · 1905
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 1907
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Boosting privately: Privacy-preserving federated extreme boosting for mobile crowdsensing
Yang Liu, Zhuo Ma, Ximeng Liu, Siqi Ma, Surya Nepal, and Robert Deng · 1907
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Improving federated learning personalization via model agnostic meta learning
Yihan Jiang, Jakub Konečnỳ, Keith Rush, and Sreeram Kannan · 1909
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A communication efficient vertical federated learning framework
Yang Liu, Yan Kang, Xinwei Zhang, Liping Li, Yong Cheng, Tianjian Chen, Mingyi Hong, and Qiang Yang · 1912
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How to share a secret
Adi Shamir · 1979
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The dining cryptographers problem: Unconditional sender and recipient untraceability
David Chaum · 1988
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Federated database systems for managing distributed, heterogeneous, and autonomous databases
Amit P Sheth and James A Larson · 1990
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Multitask learning
Rich Caruana · 1997
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Secure multi-party computation
Oded Goldreich · 1998
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Practical byzantine fault tolerance
Miguel Castro, Barbara Liskov, et al · 1999
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Privacy preserving auctions and mechanism design
Moni Naor, Benny Pinkas, and Reuban Sumner · 1999
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Public-key cryptosystems based on composite degree residuosity classes
Pascal Paillier · 1999
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Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2002
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An incentive compatible reputation mechanism
R. Jurca and B. Faltings · 2003
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Locality-sensitive hashing scheme based on p-stable distributions
Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S Mirrokni · 2004
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Privacy-preserving distributed mining of association rules on horizontally partitioned data
Murat Kantarcioglu and Chris Clifton · 2004
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Privacy preserving regression modelling via distributed computation
Ashish P Sanil, Alan F Karr, Xiaodong Lin, and Jerome P Reiter · 2004
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Local differential privacy based federated learning for internet of things
Yang Zhao, Jun Zhao, Mengmeng Yang, Teng Wang, Ning Wang, Lingjuan Lyu, Dusit Niyato, and Kwok Yan Lam · 2004
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Privacy-preserving svm using nonlinear kernels on horizontally partitioned data
Hwanjo Yu, Xiaoqian Jiang, and Jaideep Vaidya · 2006
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Fedml: A research library and benchmark for federated machine learning
Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, et al · 2007
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Generalized α \alpha -fair resource allocation in wireless networks
Eitan Altman, Konstantin Avrachenkov, and Andrey Garnaev · 2008
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Protecting privacy using k-anonymity
Khaled El Emam and Fida Kamal Dankar · 2008
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Benchmarking semi-supervised federated learning
Zhengming Zhang, Zhewei Yao, Yaoqing Yang, Yujun Yan, Joseph E Gonzalez, and Michael W Mahoney · 2008
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Privacy-preserving analysis of vertically partitioned data using secure matrix products
Alan F Karr, Xiaodong Lin, Ashish P Sanil, and Jerome P Reiter · 2009
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Achieving a nationwide learning health system
Charles P Friedman, Adam K Wong, and David Blumenthal · 2010
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Model-agnostic round-optimal federated learning via knowledge transfer
Qinbin Li, Bingsheng He, and Dawn Song · 2010
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Differential privacy via wavelet transforms
Xiaokui Xiao, Guozhang Wang, and Johannes Gehrke · 2010
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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Cloud federation
Tobias Kurze, Markus Klems, David Bermbach, Alexander Lenk, Stefan Tai, and Marcel Kunze · 2011
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Gpumlib: An efficient open-source gpu machine learning library
Noel Lopes and Bernardete Ribeiro · 2011
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Data matching: concepts and techniques for record linkage, entity resolution, and duplicate detection
Peter Christen · 2012
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CUDA programming: a developer’s guide to parallel computing with GPUs
Shane Cook · 2012
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A tutorial on bayesian nonparametric models
Samuel J Gershman and David M Blei · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Ensemble learning
Robi Polikar · 2012
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Lstm neural networks for language modeling
Martin Sundermeyer, Ralf Schlüter, and Hermann Ney · 2012
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The singapore personal data protection act and an assessment of future trends in data privacy reform
Warren B Chik · 2013
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More effective distributed ml via a stale synchronous parallel parameter server
Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B Gibbons, Garth A Gibson, Greg Ganger, and Eric P Xing · 2013
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Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
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Privacy-preserving ridge regression on hundreds of millions of records
Valeria Nikolaenko, Udi Weinsberg, Stratis Ioannidis, Marc Joye, Dan Boneh, and Nina Taft · 2013
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Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Performance evaluation of websocket protocol for implementation of full-duplex web streams
Dejan Skvorc, Matija Horvat, and Sinisa Srbljic · 2014
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Heterogeneous differential privacy
Mohammad Alaggan, Sébastien Gambs, and Anne-Marie Kermarrec · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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A comprehensive comparison of multiparty secure additions with differential privacy
Slawomir Goryczka and Li Xiong · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Heterospark: A heterogeneous cpu/gpu spark platform for machine learning algorithms
Peilong Li, Yan Luo, Ning Zhang, and Yu Cao · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Trusted execution environment: what it is, and what it is not
Mohamed Sabt, Mohammed Achemlal, and Abdelmadjid Bouabdallah · 2015
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Blockchain: Blueprint for a new economy
Melanie Swan · 2015
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Security and privacy issues of fog computing: A survey
Shanhe Yi, Zhengrui Qin, and Qun Li · 2015
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Decentralizing privacy: Using blockchain to protect personal data
G. Zyskind, O. Nathan, and A. ’. Pentland · 2015
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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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How the gdpr will change the world
Jan Philipp Albrecht · 2016
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Data poisoning attacks against autoregressive models
Scott Alfeld, Xiaojin Zhu, and Paul Barford · 2016
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Practical secure aggregation for federated learning on user-held data
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2016
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Bitcoin-ng: A scalable blockchain protocol
Ittay Eyal, Adem Efe Gencer, Emin Gun Sirer, and Robbert Van Renesse · 2016
Cited alongside, same era.
Lstm: A search space odyssey
Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber · 2016
Cited alongside, same era.
Data poisoning attacks on factorization-based collaborative filtering
Bo Li, Yining Wang, Aarti Singh, and Yevgeniy Vorobeychik · 2016
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
Cited alongside, same era.
Oblivious multi-party machine learning on trusted processors
Olga Ohrimenko, Felix Schuster, Cédric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, and Manuel Costa · 2016
Cited alongside, same era.
An overview of fog computing and its security issues
Federated collaborative filtering for privacy-preserving personalized recommendation system
Muhammad Ammad-ud din, Elena Ivannikova, Suleiman A Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, and Adrian Flanagan · 2019
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
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Secure federated matrix factorization
Di Chai, Leye Wang, Kai Chen, and Qiang Yang · 2019
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Secureboost: A lossless federated learning framework
Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, and Qiang Yang · 2019
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Ivan Stojmenovic, Sheng Wen, Xinyi Huang, and Hao Luan · 2016
Cited alongside, same era.
Modeldb: a system for machine learning model management
Manasi Vartak, Harihar Subramanyam, Wei-En Lee, Srinidhi Viswanathan, Saadiyah Husnoo, Samuel Madden, and Matei Zaharia · 2016
Cited alongside, same era.
A survey on entity alignment of knowledge base
Zhuang Yan, Li Guoliang, and Feng Jianhua · 2016
Cited alongside, same era.
Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, Rachid Guerraoui, Julien Stainer, et al · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Privacy-preserving classification on deep neural network
Hervé Chabanne, Amaury de Wargny, Jonathan Milgram, Constance Morel, and Emmanuel Prouff · 2017
Cited alongside, same era.
Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
Cited alongside, same era.
Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis, Issa Sylla, Yoonyoung Park, Grace Hsu, and Amar Das · 2019
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Variational federated multi-task learning
Luca Corinzia and Joachim M Buhmann · 2019
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Moming Duan · 2019
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Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou · 2019
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Edge aibench: Towards comprehensive end-to-end edge computing benchmarking
Tianshu Hao, Yunyou Huang, Xu Wen, Wanling Gao, Fan Zhang, Chen Zheng, Lei Wang, Hainan Ye, Kai Hwang, Zujie Ren, et al · 2019
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Fdml: A collaborative machine learning framework for distributed features
Yaochen Hu, Di Niu, Jianming Yang, and Shengping Zhou · 2019
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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, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Federated learning for keyword spotting
David Leroy, Alice Coucke, Thibaut Lavril, Thibault Gisselbrecht, and Joseph Dureau · 2019
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Federated learning in mobile edge networks: A comprehensive survey, 2019
Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, and Chunyan Miao · 2019
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Real-world image datasets for federated learning
Jiahuan Luo, Xueyang Wu, Yun Luo, Anbu Huang, Yunfeng Huang, Yang Liu, and Qiang Yang · 2019
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Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
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Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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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
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Federated learning for wireless communications: Motivation, opportunities and challenges
Solmaz Niknam, Harpreet S Dhillon, and Jeffery H Reed · 2019
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Client selection for federated learning with heterogeneous resources in mobile edge
Takayuki Nishio and Ryo Yonetani · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Privacy-aware service placement for mobile edge computing via federated learning
Yongfeng Qian, Long Hu, Jing Chen, Xin Guan, Mohammad Mehedi Hassan, and Abdulhameed Alelaiwi · 2019
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Motivating workers in federated learning: A stackelberg game perspective, 2019
Yunus Sarikaya and Ozgur Ercetin · 2019
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Robust and communication-efficient federated learning from non-iid data
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek · 2019
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Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
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Differentially private learning with adaptive clipping
Om Thakkar, Galen Andrew, and H Brendan McMahan · 2019
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A hybrid approach to privacy-preserving federated learning
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou · 2019
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Measure contribution of participants in federated learning
Guan Wang, Charlie Xiaoqian Dang, and Ziye Zhou · 2019
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Beyond inferring class representatives: User-level privacy leakage from federated learning
Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, and Hairong Qi · 2019
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Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive
Jiasi Weng, Jian Weng, Jilian Zhang, Ming Li, Yue Zhang, and Weiqi Luo · 2019
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Hybridalpha: An efficient approach for privacy-preserving federated learning
Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, and Heiko Ludwig · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Hybrid-fl: Cooperative learning mechanism using non-iid data in wireless networks
Naoya Yoshida, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto, and Ryo Yonetani · 2019
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Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang, and Yasaman Khazaeni · 2019
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Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, and Dusit Niyato · 2019
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Multi-objective evolutionary federated learning
Hangyu Zhu and Yaochu Jin · 2019
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Federated learning: A survey on enabling technologies, protocols, and applications
Mohammed Aledhari, Rehma Razzak, Reza M Parizi, and Fahad Saeed · 2020
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How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2020
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Secure single-server aggregation with (poly) logarithmic overhead
James Henry Bell, Kallista A Bonawitz, Adrià Gascón, Tancrède Lepoint, and Mariana Raykova · 2020
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Fedeval: A benchmark system with a comprehensive evaluation model for federated learning
Di Chai, Leye Wang, Kai Chen, and Qiang Yang · 2020
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A training-integrity privacy-preserving federated learning scheme with trusted execution environment
Yu Chen, Fang Luo, Tong Li, Tao Xiang, Zheli Liu, and Jin Li · 2020
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Federated bayesian optimization via thompson sampling
Zhongxiang Dai, Kian Hsiang Low, and Patrick Jaillet · 2020
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Personalized federated learning with moreau envelopes
Canh T Dinh, Nguyen H Tran, and Tuan Dung Nguyen · 2020
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Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
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Local model poisoning attacks to byzantine-robust federated learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong · 2020
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Inverting gradients–how easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller · 2020
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An efficient framework for clustered federated learning
Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran · 2020
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Federated learning of a mixture of global and local models
Filip Hanzely and Peter Richtárik · 2020
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Lower bounds and optimal algorithms for personalized federated learning
Filip Hanzely, Slavomír Hanzely, Samuel Horváth, and Peter Richtárik · 2020
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The oarf benchmark suite: Characterization and implications for federated learning systems
Sixu Hu, Yuan Li, Xu Liu, Qinbin Li, Zhaomin Wu, and Bingsheng He · 2020
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Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
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Attacks to federated learning: Responsive web user interface to recover training data from user gradients
Hans Albert Lianto, Yang Zhao, and Jun Zhao · 2020
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Ensemble distillation for robust model fusion in federated learning
Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi · 2020
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Evaluation framework for large-scale federated learning
Lifeng Liu, Fengda Zhang, Jun Xiao, and Chao Wu · 2020
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Threats to federated learning: A survey
Lingjuan Lyu, Han Yu, and Qiang Yang · 2020
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Throughput-optimal topology design for cross-silo federated learning
Othmane Marfoq, Chuan Xu, Giovanni Neglia, and Richard Vidal · 2020
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Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2020
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Robust federated learning: The case of affine distribution shifts
Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani, and Ali Jadbabaie · 2020
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A federated recommender system for online services
Ben Tan, Bo Liu, Vincent Zheng, and Qiang Yang · 2020
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Federated meta-learning for fraudulent credit card detection
Wenbo Zheng, Lan Yan, Chao Gou, and Fei-Yue Wang · 2020
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Performance optimization of federated person re-identification via benchmark analysis
Weiming Zhuang, Yonggang Wen, Xuesen Zhang, Xin Gan, Daiying Yin, Dongzhan Zhou, Shuai Zhang, and Shuai Yi · 2020
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End-to-end privacy preserving deep learning on multi-institutional medical imaging
Georgios Kaissis, Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin, Andrew Trask, Ionésio Lima, Jason Mancuso, Friederike Jungmann, Marc-Matthias Steinborn, et al · 2021
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Collaborative federated learning for healthcare: Multi-modal covid-19 diagnosis at the edge, 2021
Adnan Qayyum, Kashif Ahmad, Muhammad Ahtazaz Ahsan, Ala Al-Fuqaha, and Junaid Qadir · 2021
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Blockchain-based federated learning in mobile edge networks with application in internet of vehicles
Rui Wang, Heju Li, and Erwu Liu · 2021
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Inprivate digging: Enabling tree-based distributed data mining with differential privacy
Lingchen Zhao, Lihao Ni, Shengshan Hu, Yaniiao Chen, Pan Zhou, Fu Xiao, and Libing Wu · 2095
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