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Federated learning (FL) has emerged as an effective approach to address consumer privacy needs.
Market segmentation: a review
TP Beane and DM Ennis · 1987
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Anantha M Prasad, Louis R Iverson, and Andy Liaw · 2006
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Cynthia Dwork and Jing Lei · 2009
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Wide & deep learning for recommender systems
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Grouplens · 2016
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Practical secure aggregation for privacy-preserving machine learning
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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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Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
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Deep & cross network for ad click predictions
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang · 2017
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Expanding the reach of federated learning by reducing client resource requirements
Sebastian Caldas, Jakub Konečny, H Brendan McMahan, and Ameet Talwalkar · 2018
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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
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Deep interest network for click-through rate prediction
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai · 2018
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Neel Guha, Ameet Talwalkar, and Virginia Smith · 2019
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Federated learning for ranking browser history suggestions
Florian Hartmann, Sunah Suh, Arkadiusz Komarzewski, Tim D Smith, and Ilana Segall · 2019
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FedMD: Heterogenous federated learning via model distillation
Daliang Li and Junpu Wang · 2019
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Deep learning recommendation model for personalization and recommendation systems
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G Azzolini, et al · 2019
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Split learning for collaborative deep learning in healthcare
Maarten G Poirot, Praneeth Vepakomma, Ken Chang, Jayashree Kalpathy-Cramer, Rajiv Gupta, and Ramesh Raskar · 2019
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A hybrid approach to privacy-preserving federated learning - (extended abstract)
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou · 2019
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A survey on federated learning: The journey from centralized to distributed on-site learning and beyond
Sawsan AbdulRahman, Hanine Tout, Hakima Ould-Slimane, Azzam Mourad, Chamseddine Talhi, and Mohsen Guizani · 2020
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FedBE: Making Bayesian model ensemble applicable to federated learning
Hong-You Chen and Wei-Lun Chao · 2020
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FedBoost: A communication-efficient algorithm for federated learning
Jenny Hamer, Mehryar Mohri, and Ananda Theertha Suresh · 2020
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How Apple personalizes Siri without hoovering up your data
Karen Hao · 2020
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Group knowledge transfer: Federated learning of large CNNs at the edge
Chaoyang He, Murali Annavaram, and Salman Avestimehr · 2020
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Deep learning with label differential privacy
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, and Chiyuan Zhang · 2021
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FjORD: Fair and accurate federated learning under heterogeneous targets with ordered dropout
Samuel Horvath, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos Venieris, and Nicholas Lane · 2021
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AutoFL: Enabling heterogeneity-aware energy efficient federated learning
Young Geun Kim and Carole-Jean Wu · 2021
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FedXGBoost: Privacy-preserving XGBoost for federated learning
Nhan Khanh Le, Yang Liu, Quang Minh Nguyen, Qingchen Liu, Fangzhou Liu, Quanwei Cai, and Sandra Hirche · 2021
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A novel CTR prediction model based on DeepFM for Taobao data
LinShu Li, Jianbo Hong, Sitao Min, and Yunfan Xue · 2021
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Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi · 2020
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Blockchain-assisted ensemble federated learning for automatic modulation classification in wireless networks
Umer Majeed and Choong Seon Hong · 2020
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A more private way to measure ad conversions, the event conversion measurement API, October 2020
Maud Nalpas and Sam Dutton · 2020
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Billion-scale federated learning on mobile clients: A submodel design with tunable privacy
Chaoyue Niu, Fan Wu, Shaojie Tang, Lifeng Hua, Rongfei Jia, Chengfei Lv, Zhihua Wu, and Guihai Chen · 2020
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Federated ensemble regression using classification
Oghenejokpeme I Orhobor, Larisa N Soldatova, and Ross D King · 2020
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The future of digital health with federated learning
Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger R Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N Galtier, Bennett A Landman, Klaus Maier-Hein, et al · 2020
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Ad display/click data on taobao.com, 2020
Pavan Sabnagapati · 2020
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Understanding capacity-driven scale-out neural recommendation inference
Michael Lui, Yavuz Yetim, Özgür Özkan, Zhuoran Zhao, Shin-Yeh Tsai, Carole-Jean Wu, and Mark Hempstead · 2021
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Research on privacy protection of multi source data based on improved GBDT federated ensemble method with different metrics
Changyin Luo, Xuebin Chen, Jingcheng Xu, and Shufen Zhang · 2021
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Antipodes of label differential privacy: PATE and ALIBI
Mani Malek, Ilya Mironov, Karthik Prasad, Igor Shilov, and Florian Tramer · 2021
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Flatee: Federated learning across trusted execution environments
Arup Mondal, Yash More, Ruthu Hulikal Rooparaghunath, and Debayan Gupta · 2021
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Federated learning with buffered asynchronous aggregation
John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Michael Rabbat, Mani Malek, and Dzmitry Huba · 2021
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Unleashing the tiger: Inference attacks on split learning
Dario Pasquini, Giuseppe Ateniese, and Massimo Bernaschi · 2021
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Federated evaluation and tuning for on-device personalization: System design & applications
Matthias Paulik, Matt Seigel, Henry Mason, Dominic Telaar, Joris Kluivers, Rogier van Dalen, Chi Wai Lau, Luke Carlson, Filip Granqvist, Chris Vandevelde, et al · 2021
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Masked LARk: Masked learning, aggregation and reporting workflow
Joseph J Pfeiffer III, Denis Charles, Davis Gilton, Young Hun Jung, Mehul Parsana, and Erik Anderson · 2021
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Fed-ensemble: Improving generalization through model ensembling in federated learning
Naichen Shi, Fan Lai, Raed Al Kontar, and Mosharaf Chowdhury · 2021
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RecSSD: Near data processing for solid state drive based recommendation inference
Mark Wilkening, Udit Gupta, Samuel Hsia, Caroline Trippel, Carole-Jean Wu, David Brooks, and Gu-Yeon Wei · 2021
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Heterogeneous ensemble knowledge transfer for training large models in federated learning
Yae Jee Cho, Andre Manoel, Gauri Joshi, Robert Sim, and Dimitrios Dimitriadis · 2022
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Preserving privacy in federated learning with ensemble cross-domain knowledge distillation
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Papaya: Practical, private, and scalable federated learning
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Towards fair federated recommendation learning: Characterizing the inter-dependence of system and data heterogeneity
Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, and Carole-Jean Wu · 2022
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