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Federated learning (FL) enables distributed resource-constrained devices to jointly train shared models while keeping the training data local for privacy purposes.
Completeness theorems for non-cryptographic fault-tolerant distributed computation
Michael Ben-Or, Shafi Goldwasser, and Avi Wigderson · 1988
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Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization
Ciyou Zhu, Richard H Byrd, Peihuang Lu, and Jorge Nocedal · 1997
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Nus-wide: a real-world web image database from national university of singapore
Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Frank D McSherry · 2009
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Distributed optimization and statistical learning via the alternating direction method of multipliers
Stephen Boyd, Neal Parikh, and Eric Chu · 2011
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A firm foundation for private data analysis
Cynthia Dwork · 2011
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 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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Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems
Mingyi Hong, Zhi-Quan Luo, and Meisam Razaviyayn · 2016
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" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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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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Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 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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Rényi differential privacy
Ilya Mironov · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
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cpsgd: communication-efficient and differentially-private distributed sgd
Naman Agarwal, Ananda Theertha Suresh, Felix Yu, Sanjiv Kumar, and H Brendan McMahan · 2018
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Protection against reconstruction and its applications in private federated learning
Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers · 2018
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Federated learning of predictive models from federated electronic health records
Theodora S Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Ch Paschalidis, and Wei Shi · 2018
Cited alongside, same era.
Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
Cited alongside, same era.
Learning differentially private recurrent language models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
Cited alongside, same era.
Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
Cited alongside, same era.
Sok: Security and privacy in machine learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael P Wellman · 2018
Cited alongside, same era.
Backdoor attacks and defenses in feature-partitioned collaborative learning
Yang Liu, Zhihao Yi, and Tianjian Chen · 2020
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Privacy preserving vertical federated learning for tree-based models
Yuncheng Wu, Shaofeng Cai, Xiaokui Xiao, Gang Chen, and Beng Chin Ooi · 2020
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No routing needed between capsules
Adam Byerly, Tatiana Kalganova, and Ian Dear · 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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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
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DeepSecure: Scalable provably-secure deep learning
Bita Darvish Rouhani, M Sadegh Riazi, and Farinaz Koushanfar · 2018
Cited alongside, same era.
A deeper look at 3d shape classifiers
Jong-Chyi Su, Matheus Gadelha, Rui Wang, and Subhransu Maji · 2018
Cited alongside, same era.
Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar · 2018
Cited alongside, same era.
Applied federated learning: Improving google keyboard query suggestions
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays · 2018
Cited alongside, same era.
Differentially private robust admm for distributed machine learning
Jiahao Ding, Xinyue Zhang, Mingsong Chen, Kaiping Xue, Chi Zhang, and Miao Pan · 2019
Cited alongside, same era.
Jinshuo Dong, Aaron Roth, and Weijie J Su · 2019
Cited alongside, same era.
Learning privately over distributed features: An admm sharing approach
Yaochen Hu, Peng Liu, Linglong Kong, and Di Niu · 2019
Cited alongside, same era.
Prashant Gohel, Priyanka Singh, and Manoranjan Mohanty · 2021
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Catastrophic data leakage in vertical federated learning
Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, and Tianyi Chen · 2021
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Ditto: Fair and robust federated learning through personalization
Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith · 2021
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Antipodes of label differential privacy: Pate and alibi
Mani Malek Esmaeili, Ilya Mironov, Karthik Prasad, Igor Shilov, and Florian Tramer · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Feature generation for long-tail classification
Rahul Vigneswaran, Marc T Law, Vineeth N Balasubramanian, and Makarand Tapaswi · 2021
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Datalens: Scalable privacy preserving training via gradient compression and aggregation
Boxin Wang, Fan Wu, Yunhui Long, Luka Rimanic, Ce Zhang, and Bo Li · 2021
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To be robust or to be fair: Towards fairness in adversarial training
Han Xu, Xiaorui Liu, Yaxin Li, Anil Jain, and Jiliang Tang · 2021
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Inexact-admm based federated meta-learning for fast and continual edge learning
Sheng Yue, Ju Ren, Jiang Xin, Sen Lin, and Junshan Zhang · 2021
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Secure bilevel asynchronous vertical federated learning with backward updating
Qingsong Zhang, Bin Gu, Cheng Deng, and Heng Huang · 2021
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Compressed-vfl: Communication-efficient learning with vertically partitioned data
Timothy J Castiglia, Anirban Das, Shiqiang Wang, and Stacy Patterson · 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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Towards communication-efficient vertical federated learning training via cache-enabled local updates
Fangcheng Fu, Xupeng Miao, Jiawei Jiang, Huanran Xue, and Bin Cui · 2022
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Fedbcd: A communication-efficient collaborative learning framework for distributed features
Yang Liu, Xinwei Zhang, Yan Kang, Liping Li, Tianjian Chen, Mingyi Hong, and Qiang Yang · 2022
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Differentially private vertical federated learning
Thilina Ranbaduge and Ming Ding · 2022
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Multi-view dual attention network for 3d object recognition
Wenju Wang, Yu Cai, and Tao Wang · 2022
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Adaptive vertical federated learning on unbalanced features
Jie Zhang, Song Guo, Zhihao Qu, Deze Zeng, Haozhao Wang, Qifeng Liu, and Albert Y Zomaya · 2022
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Flexible vertical federated learning with heterogeneous parties
Timothy Castiglia, Shiqiang Wang, and Stacy Patterson · 2023
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How to dp-fy ml: A practical guide to machine learning with differential privacy
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Guha Thakurta · 2023
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Fairness-aware class imbalanced learning on multiple subgroups
Davoud Ataee Tarzanagh, Bojian Hou, Boning Tong, Qi Long, and Li Shen · 2023
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Privacy tradeoffs in vertical federated learning
Linh Tran, Timothy Castiglia, Stacy Patterson, and Ana Milanova · 2023
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Unraveling the connections between privacy and certified robustness in federated learning against poisoning attacks
Chulin Xie, Yunhui Long, Pin-Yu Chen, Qinbin Li, Sanmi Koyejo, and Bo Li · 2023
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Sok: Privacy-preserving data synthesis
Yuzheng Hu, Fan Wu, Qinbin Li, Yunhui Long, Gonzalo Munilla Garrido, Chang Ge, Bolin Ding, David Forsyth, Bo Li, and Dawn Song · 2024
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