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Increasing concerns and regulations about data privacy and sparsity necessitate the study of privacy-preserving, decentralized learning methods for natural language processing (NLP) tasks.
Newsweeder: Learning to filter netnews
Ken Lang. 1995 · 1995
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Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction, AKBC-WEKEX@NAACL-HLT 2012, Montrèal, Canada, June 7-8, 2012
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2017b · 2017
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Regulation eu 2016/679 of the european parliament and of the council of 27 april 2016
General Data Protection Regulation. 2016 · 2017
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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S. Talwalkar. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Andrew Hard, K. Rao, Rajiv Mathews, F. Beaufays, S. Augenstein, Hubert Eichner, Chloé Kiddon, and D. Ramage. 2018 · 2018
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A generic framework for privacy preserving deep learning
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
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MRQA 2019 shared task: Evaluating generalization in reading comprehension
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Central server free federated learning over single-sided trust social networks
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TensorFlow Federated
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Advances and open problems in federated learning
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Natural questions: A benchmark for question answering research
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D. Liu and T. Miller. 2020 · 2020
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Nvidia clara
NVIDIA. 2019 · 2019
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Fedgraphnn: A federated learning system and benchmark for graph neural networks
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