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Federated learning allows distributed users to collaboratively train a model while keeping each user's data private.
Speech understanding systems: A summary of results of the five-year research effort
Reddy, D. R. et al · 1977
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The enron corpus: A new dataset for email classification research
Klimt, B. and Yang, Y · 2004
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ROUGE: A package for automatic evaluation of summaries
Lin, C.-Y · 2004
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Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Cernockỳ, J., and Khudanpur, S · 2010
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Artificial Intelligence: A Modern Approach
Russell, S. and Norvig, P · 2010
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On the properties of neural machine translation: Encoder–decoder approaches
Cho, K., van Merriënboer, B., Bahdanau, D., and Bengio, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 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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Practical secure aggregation for federated learning on user-held data
Bonawitz, K. A., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K · 2016
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Diverse beam search: Decoding diverse solutions from neural sequence models
Vijayakumar, A. K., Cogswell, M., Selvaraju, R. R., Sun, Q., Lee, S., Crandall, D., and Batra, D · 2016
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A closer look at memorization in deep networks
Arpit, D., Jastrzebski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., et al · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., et al · 2017
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Pointer sentinel mixture models
Merity, S., Xiong, C., Bradbury, J., and Socher, R · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Federated learning for mobile keyboard prediction
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D · 2018
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Privacy-preserving deep learning via additively homomorphic encryption
Phong, L. T., Aono, Y., Hayashi, T., Wang, L., and Moriai, S · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I · 2018
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2018
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Publicly available clinical BERT embeddings
Alsentzer, E., Murphy, J. R., Boag, W., Weng, W.-H., Jin, D., Naumann, T., and McDermott, M · 2019
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., and Song, D · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Cited alongside, same era.
ClinicalBERT: Modeling clinical notes and predicting hospital readmission
Huang, K., Altosaar, J., and Ranganath, R · 2019
Cited alongside, same era.
Exploiting unintended feature leakage in collaborative learning
Melis, L., Song, C., De Cristofaro, E., and Shmatikov, V · 2019
Cited alongside, same era.
When the curious abandon honesty: Federated learning is not private
Boenisch, F., Dziedzic, A., Schuster, R., Shamsabadi, A. S., Shumailov, I., and Papernot, N · 2021
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Extracting training data from large language models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al · 2021
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Tag: Gradient attack on transformer-based language models
Deng, J., Wang, Y., Li, J., Wang, C., Shang, C., Liu, H., Rajasekaran, S., and Ding, C · 2021
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Fidel: Reconstructing private training samples from weight updates in federated learning
Enthoven, D. and Al-Ars, Z · 2021
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Membership inference attacks on deep regression models for neuroimaging
Gupta, U., Stripelis, D., Lam, P. K., Thompson, P., Ambite, J. L., and Ver Steeg, G · 2021
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
Cited alongside, same era.
Deep leakage from gradients
Zhu, L., Liu, Z., and Han, S · 2019
Cited alongside, same era.
Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
Inverting gradients–how easy is it to break privacy in federated learning?
Geiping, J., Bauermeister, H., Dröge, H., and Moeller, M · 2020
Cited alongside, same era.
The curious case of neural text degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y · 2020
Cited alongside, same era.
spaCy: Industrial-strength Natural Language Processing in Python
Honnibal, M., Montani, I., Van Landeghem, S., and Boyd, A · 2020
Cited alongside, same era.
Federated pretraining and fine tuning of BERT using clinical notes from multiple silos
Liu, D. and Miller, T · 2020
Cited alongside, same era.
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Evaluating gradient inversion attacks and defenses in federated learning
Huang, Y., Gupta, S., Song, Z., Li, K., and Arora, S · 2021
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Gradient inversion with generative image prior
Jeon, J., Lee, K., Oh, S., Ok, J., et al · 2021
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Catastrophic data leakage in vertical federated learning
Jin, X., Chen, P.-Y., Hsu, C.-Y., Yu, C.-M., and Chen, T · 2021
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MedGPT: Medical concept prediction from clinical narratives
Kraljevic, Z., Shek, A., Bean, D., Bendayan, R., Teo, J., and Dobson, R · 2021
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Large language models can be strong differentially private learners
Li, X., Tramer, F., Liang, P., and Hashimoto, T · 2021
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Studying word order through iterative shuffling
Malkin, N., Lanka, S., Goel, P., and Jojic, N · 2021
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Understanding unintended memorization in federated learning
Thakkar, O., Ramaswamy, S., Mathews, R., and Beaufays, F · 2021
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User label leakage from gradients in federated learning
Wainakh, A., Ventola, F., Müßig, T., Keim, J., Cordero, C. G., Zimmer, E., Grube, T., Kersting, K., and Mühlhäuser, M · 2021
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See through gradients: Image batch recovery via gradinversion
Yin, H., Mallya, A., Vahdat, A., Alvarez, J. M., Kautz, J., and Molchanov, P · 2021
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Differentially private fine-tuning of language models
Yu, D., Naik, S., Backurs, A., Gopi, S., Inan, H. A., Kamath, G., Kulkarni, J., Lee, Y. T., Manoel, A., Wutschitz, L., et al · 2021
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R-gap: Recursive gradient attack on privacy
Zhu, J. and Blaschko, M. B · 2021
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Lamp: Extracting text from gradients with language model priors
Dimitrov, D. I., Balunović, M., Jovanović, N., and Vechev, M · 2022
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Decepticons: Corrupted transformers breach privacy in federated learning for language models
Fowl, L., Geiping, J., Reich, S., Wen, Y., Czaja, W., Goldblum, M., and Goldstein, T · 2022
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