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Federated learning enables the deployment of machine learning to problems for which centralized data collection is impractical.
Some methods for classification and analysis of multivariate observations
James MacQueen et al · 1967
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Handwritten digit recognition with a back-propagation network
Yann LeCun, Bernhard Boser, John Denker, Donnie Henderson, Richard Howard, Wayne Hubbard, and Lawrence Jackel · 1989
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The self-organizing map
Teuvo Kohonen · 1990
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Distance metric learning for large margin nearest neighbor classification
Kilian Q Weinberger, John Blitzer, and Lawrence Saul · 2005
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Embedding-based speaker adaptive training of deep neural networks
Xiaodong Cui, Vaibhava Goel, and George Saon · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 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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Evaluating nfl player health and performance: legal and ethical issues
Jessica L Roberts, I Glenn Cohen, Christopher R Deubert, and Holly Fernandez Lynch · 2017
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Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S Zemel · 2017
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Distributed learning of deep neural network over multiple agents
Otkrist Gupta and Ramesh Raskar · 2018
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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Imitation learning from visual data with multiple intentions
Aviv Tamar, Khashayar Rohanimanesh, Yinlam Chow, Chris Vigorito, Ben Goodrich, Michael Kahane, and Derik Pridmore · 2018
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Federated learning with personalization layers
Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary · 2019
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Ethical and legal issues of ingestible electronic sensors
Sara Gerke, Timo Minssen, Helen Yu, and I Glenn Cohen · 2019
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Learning a multi-modal policy via imitating demonstrations with mixed behaviors
Fang-I Hsiao, Jui-Hsuan Kuo, and Min Sun · 2019
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Improving federated learning personalization via model agnostic meta learning
Yihan Jiang, Jakub Konečnỳ, Keith Rush, and Sreeram Kannan · 2019
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Fedmd: Heterogenous federated learning via model distillation
Ethical and legal challenges of artificial intelligence-driven healthcare
Sara Gerke, Timo Minssen, and Glenn Cohen · 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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Secure federated averaging algorithm with differential privacy
Yiwei Li, Tsung-Hui Chang, and Chong-Yung Chi · 2020
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Interpretable and personalized apprenticeship scheduling: Learning interpretable scheduling policies from heterogeneous user demonstrations
Rohan Paleja, Andrew Silva, Letian Chen, and Matthew Gombolay · 2020
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Daliang Li and Junpu Wang · 2019
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Mirroring to build trust in digital assistants
Katherine Metcalf, Barry-John Theobald, Garrett Weinberg, Robert Lee, Ing-Marie Jonsson, Russ Webb, and Nicholas Apostoloff · 2019
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Embeddings for dnn speaker adaptive training
Joanna Rownicka, Peter Bell, and Steve Renals · 2019
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Characterizing and avoiding negative transfer
Zirui Wang, Zihang Dai, Barnabás Póczos, and Jaime Carbonell · 2019
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Libritts: A corpus derived from librispeech for text-to-speech
Heiga Zen, Rob Clark, Ron J. Weiss, Viet Dang, Ye Jia, Yonghui Wu, Yu Zhang, and Zhifeng Chen · 2019
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Federated learning with gaussian differential privacy
Zhou Chuanxin, Sun Yi, and Wang Degang · 2020
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Adaptive personalized federated learning
Yuyang Deng, Mohammad Mahdi Kamani, and Mehrdad Mahdavi · 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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Exploiting shared representations for personalized federated learning
Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai · 2021
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Sapaugment: Learning a sample adaptive policy for data augmentation
Ting-Yao Hu, Ashish Shrivastava, Jen-Hao Rick Chang, Hema Koppula, Stefan Braun, Kyuyeon Hwang, Ozlem Kalinli, and Oncel Tuzel · 2021
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Differentially private model personalization
Prateek Jain, Keith Rush, Adam Smith, Shuang Song, and Abhradeep Thuakurta · 2021
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Spatio-temporal split learning
Joongheon Kim, Seunghoon Park, Soyi Jung, and Seehwan Yoo · 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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Personalized federated deep learning for pain estimation from face images
Ognjen Rudovic, Nicolas Tobis, Sebastian Kaltwang, Björn Schuller, Daniel Rueckert, Jeffrey F Cohn, and Rosalind W Picard · 2021
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Personalized federated learning with clustered generalization
Xueyang Tang, Song Guo, and Jingcai Guo · 2021
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