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Federated learning (FL) is a promising approach to distributed compute, as well as distributed data, and provides a level of privacy and compliance to legal frameworks.
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End-to-end open-domain question answering with bertserini
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Utilizing bert for aspect-based sentiment analysis via constructing auxiliary sentence
Chi Sun, Luyao Huang, and Xipeng Qiu. 2019a · 1903
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Docbert: Bert for document classification
Ashutosh Adhikari, Achyudh Ram, Raphael Tang, and Jimmy Lin. 2019 · 1904
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Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith. 2019 · 1905
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Measuring the effects of non-identical data distribution for federated visual classification
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Scaffold: Stochastic controlled averaging for on-device federated learning
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Huggingface’s transformers: State-of-the-art natural language processing
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Fedner: Medical named entity recognition with federated learning
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Ensemble distillation for robust model fusion in federated learning
Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi. 2020 · 2006
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
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Federated optimization: Distributed optimization beyond the datacenter
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Character-level convolutional networks for text classification
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Attention is all you need
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 2019
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 2020
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Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage. 2018 · 2018
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Natural language processing for ehr-based computational phenotyping
Zexian Zeng, Yu Deng, Xiaoyu Li, Tristan Naumann, and Yuan Luo. 2018 · 2018
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018 · 2018
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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. 2017a
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Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2017b
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How to fine-tune bert for text classification?
Chi Sun, Xipeng Qiu, Yige Xu, and Xuanjing Huang. 2019b
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Biobert: a pre-trained biomedical language representation model for biomedical text mining
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Federated learning using a mixture of experts
Edvin Listo Zec, Olof Mogren, John Martinsson, Leon René Sütfeld, and Daniel Gillblad. 2020 · 2020
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Federated pretraining and fine tuning of bert using clinical notes from multiple silos
Dianbo Liu and Tim Miller. 2020 · 2020
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