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Natural Language Processing (NLP) techniques can be applied to help with the diagnosis of medical conditions such as depression, using a collection of a person's utterances.
A neural probabilistic language model
Bengio, Y., Ducharme, R., Vincent, P., and Janvin, C · 2003
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Li, Y., Baldwin, T., and Cohn, T · 2005
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Our data, ourselves: Privacy via distributed noise generation
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., and Naor, M · 2006
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Collobert, R. and Weston, J · 2008
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Differential privacy: A survey of results
Dwork, C · 2008
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Differential privacy under fire
Haeberlen, A., Pierce, B. C., and Narayan, A · 2011
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Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., and Dean, J · 2013
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Mental health action plan 2013-2020, 2013
(WHO), W. H. O · 2013
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Jumping nlp curves: A review of natural language processing research [review article]
Cambria, E. and White, B · 2014
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The algorithmic foundations of differential privacy
Dwork, C. and Roth, A · 2014
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User review sites as a resource for large-scale sociolinguistic studies
Hovy, D., Johannsen, A., and Søgaard, A · 2015
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An analysis of the user occupational class through Twitter content
Preoţiuc-Pietro, D., Lampos, V., and Aletras, N · 2015
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
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Federated learning of deep networks using model averaging
McMahan, H. B., Moore, E., Ramage, D., and Arcas, B. A. Y · 2016
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Communication-efficient learning of deep networks from decentralized data, 2017
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
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Learning with privacy at scale, 2017
Privacy, A. D · 2017
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Practical secure aggregation for privacy-preserving machine learning
Segal, A., Marcedone, A., Kreuter, B., Ramage, D., McMahan, H. B., Seth, K., Bonawitz, K. A., Patel, S., and Ivanov, V · 2017
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Differentially private federated learning: A client level perspective, 2018
Geyer, R. C., Klein, T., and Nabi, M · 2018
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Learning differentially private recurrent language models, 2018
Evolution of the PAN Lab on Digital Text Forensics , pp. 461–485
Rosso, P., Potthast, M., Stein, B., Stamatatos, E., Rangel, F., and Daelemans, W · 2019
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Federated machine learning: Concept and applications, 2019
Yang, Q., Liu, Y., Chen, T., and Tong, Y · 2019
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Yu, L., Liu, L., Pu, C., Gursoy, M. E., and Truex, S · 2019
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Albert: A lite bert for self-supervised learning of language representations, 2020
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R · 2020
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Sanh, V., Debut, L., Chaumond, J., and Wolf, T · 2020
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Benchmarking differentially private residual networks for medical imagery
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McMahan, H. B., Ramage, D., Talwar, K., and Zhang, L · 2018
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Parameter sharing methods for multilingual self-attentional translation models, 2018
Sachan, D. S. and Neubig, G · 2018
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Federated multi-task learning, 2018
Smith, V., Chiang, C.-K., Sanjabi, M., and Talwalkar, A · 2018
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Federated learning with non-iid data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V · 2018
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You are what you tweet: Detecting depression in social media via twitter usage, 2019
Bonner, A · 2019
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Federated meta-learning with fast convergence and efficient communication, 2019
Chen, F., Luo, M., Dong, Z., Li, Z., and He, X · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Singh, S., Sikka, H., Kotti, S., and Trask, A · 2020
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Differentially private learning needs better features (or much more data)
Tramèr, F. and Boneh, D · 2020
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Depression factsheet, 2020
(WHO), W. H. O · 2020
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An investigation towards differentially private sequence tagging in a federated framework
Jana, A. and Biemann, C · 2021
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Advances and open problems in federated learning, 2021
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D’Oliveira, R. G. L., Eichner, H., Rouayheb, S. E., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., Gruteser, M., Harchaoui, Z., He, C., He, L., Huo, Z., Hutchinson, B., Hsu, J., Jaggi, M., Javidi, T., Joshi, G., Khodak, M., Konečný, J., Korolova, A., Koushanfar, F., Koyejo, S., Lepoint, T., Liu, Y., Mittal, P., Mohri, M., Nock, R., Özgür, A., Pagh, R., Raykova, M., Qi, H., Ramage, D., Raskar, R., Song, D., Song, W., Stich, S. U., Sun, Z., Suresh, A. T., Tramèr, F., Vepakomma, P., Wang, J., Xiong, L., Xu, Z., Yang, Q., Yu, F. X., Yu, H., and Zhao, S · 2021
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A survey on federated learning systems: Vision, hype and reality for data privacy and protection, 2021
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., and He, B · 2021
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Privacy regularization: Joint privacy-utility optimization in language models
Mireshghallah, F., Inan, H. A., Hasegawa, M., Rühle, V., Berg-Kirkpatrick, T., and Sim, R · 2021
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Privacy-adaptive bert for natural language understanding, 2021
Qu, C., Kong, W., Yang, L., Zhang, M., Bendersky, M., and Najork, M · 2021
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Federated learning improves site performance in multicenter deep learning without data sharing
Sarma, K. V., Harmon, S., Sanford, T., Roth, H. R., Xu, Z., Tetreault, J., Xu, D., Flores, M. G., Raman, A. G., Kulkarni, R., Wood, B. J., Choyke, P. L., Priester, A. M., Marks, L. S., Raman, S. S., Enzmann, D., Turkbey, B., Speier, W., and Arnold, C. W · 2021
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