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Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables multiple parties to jointly re-train a shared model without sharing their data with any other parties, offering advantages in both scale and privacy.
Evaluation of adaptive mixtures of competing experts
Steven J Nowlan and Geoffrey E Hinton · 1991
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Ecml-pkdd discovery challenge 2006 overview
Steffen Bickel · 2006
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Domain Adaptation for Statistical Classifiers
Hal Daumé, III and Daniel Marcu · 2006
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Differential privacy
Cynthia Dwork · 2006
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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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Learning from multiple sources
Koby Crammer, Michael Kearns, and Jennifer Wortman · 2008
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Frustratingly easy domain adaptation
Hal Daumé III · 2009
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
Mixture of Experts: A Literature Survey
Saeed Masoudnia and Reza Ebrahimpour · 2014
Cited alongside, same era.
Federated optimization: Distributed optimization beyond the datacenter
Jakub Konecný, Brendan McMahan, and Daniel Ramage · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Differentially Private Federated Learning: A Client Level Perspective
Robin C. Geyer, Tassilo Klein, and Moin Nabi · 2017
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Privacy-preserving transfer learning for knowledge sharing
Xiawei Guo, Quanming Yao, WeiWei Tu, Yuqiang Chen, Wenyuan Dai, and Qiang Yang · 2018
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Learning private neural language modeling with attentive aggregation
Shaoxiong Ji, Shirui Pan, Guodong Long, Xue Li, Jing Jiang, and Zi Huang · 2018
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An introduction to domain adaptation and transfer learning
Wouter M. Kouw and Marco Loog · 2018
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Cited alongside, same era.
Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konecný, H. Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
Cited alongside, same era.
Federated learning of deep networks using model averaging
H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas · 2016
Cited alongside, same era.
Yang Liu, Tianjian Chen, and Qiang Yang · 2018
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Scalable private learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
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