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Many problems in machine learning rely on multi-task learning (MTL), in which the goal is to solve multiple related machine learning tasks simultaneously.
How to share a secret
Adi Shamir · 1979
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Multitask learning
Rich Caruana · 1997
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Multi-task learning for stock selection
Joumana Ghosn and Yoshua Bengio · 1997
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Regularized multi-task learning
Theodoros Evgeniou and Massimiliano Pontil · 2004
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A framework for learning predictive structures from multiple tasks and unlabeled data
Rie Kubota Ando and Tong Zhang · 2005
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Learning multiple tasks with kernel methods
Theodoros Evgeniou, Charles A Micchelli, Massimiliano Pontil, and John Shawe-Taylor · 2005
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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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A convex formulation for learning task relationships in multi-task learning
Yu Zhang and Dit-Yan Yeung · 2010
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Mechanism design in large games: incentives and privacy
Michael J. Kearns, Mallesh M. Pai, Aaron Roth, and Jonathan R. Ullman · 2014
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Approximately stable, school optimal, and student-truthful many-to-one matchings (via differential privacy)
Sampath Kannan, Jamie Morgenstern, Aaron Roth, and Zhiwei Steven Wu · 2015
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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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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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Asynchronous multi-task learning
Inci M Baytas, Ming Yan, Anil K Jain, and Jiayu Zhou · 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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Differentially private multi-task learning
Sunil Kumar Gupta, Santu Rana, and Svetha Venkatesh · 2016
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Emnist: Extending mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik · 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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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Distributed multi-task relationship learning
Sulin Liu, Sinno Jialin Pan, and Qirong Ho · 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
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 2017
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Federated multi-task learning
Virginia Smith, Chaokai Chiang, Maziar Sanjabi, and Ameet Talwalkar · 2017
Cited alongside, same era.
Privacy-preserving distributed multi-task learning with asynchronous updates
Liyang Xie, Inci M Baytas, Kaixiang Lin, and Jiayu Zhou · 2017
Personalized federated learning with moreau envelopes
Canh T Dinh, Nguyen H Tran, and Tuan Dung Nguyen · 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 Richtarik · 2020
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Personalized federated learning with differential privacy
Rui Hu, Yuanxiong Guo, Hongning Li, Qingqi Pei, and Yanmin Gong · 2020
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Three approaches for personalization with applications to federated learning
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Cited alongside, same era.
A survey on multi-task learning
Yu Zhang and Qiang Yang · 2017
Cited alongside, same era.
Leaf: A benchmark for federated settings, https://leaf.cmu.edu/
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 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 tasks for multitask learning: Heterogenous patient populations in the icu
Harini Suresh, Jen J Gong, and John V Guttag · 2018
Cited alongside, same era.
Federated learning with autotuned communication-efficient secure aggregation
Keith Bonawitz, Fariborz Salehi, Jakub Konečný, Brendan McMahan, and Marco Gruteser · 2019
Cited alongside, same era.
Multitask learning and benchmarking with clinical time series data
Hrayr Harutyunyan, Hrant Khachatrian, David C Kale, Greg Ver Steeg, and Aram Galstyan · 2019
Cited alongside, same era.
Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh · 2020
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Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints
Felix Sattler, Klaus-Robert Müller, and Wojciech Samek · 2020
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A Theoretical Perspective on Differentially Private Federated Multi-task Learning
Huiwen Wu, Cen Chen, and Li Wang · 2020
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Salvaging federated learning by local adaptation
Tao Yu, Eugene Bagdasaryan, and Vitaly Shmatikov · 2020
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Differentially private model personalization
Prateek Jain, John Rush, Adam Smith, Shuang Song, and Abhradeep Guha Thakurta · 2021
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The distributed discrete gaussian mechanism for federated learning with secure aggregation
Peter Kairouz, Ziyu Liu, and Thomas Steinke · 2021
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A superquantile approach to federated learning with heterogeneous devices
Yassine Laguel, Krishna Pillutla, Jérôme Malick, and Zaid Harchaoui · 2021
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Learning with user-level privacy
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, and Ananda Theertha Suresh · 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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Private federated learning without a trusted server: Optimal algorithms for convex losses
Andrew Lowy and Meisam Razaviyayn · 2021
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Personalization improves privacy-accuracy tradeoffs in federated learning
Alberto Bietti, Chen-Yu Wei, Miroslav Dudik, John Langford, and Steven Wu · 2022
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Private non-convex federated learning without a trusted server
Andrew Lowy, Ali Ghafelebashi, and Meisam Razaviyayn · 2022
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