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Federated learning (FL) is a heavily promoted approach for training ML models on sensitive data, e.g., text typed by users on their smartphones.
Catastrophic forgetting in connectionist networks
French, R. M · 1999
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Differential privacy: A survey of results
Dwork, C · 2008
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Learning multiple layers of features from tiny images, 2009
Krizhevsky, A · 2009
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Differential privacy
Dwork, C · 2011
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
Earlier work this paper cites.
Rapid adaptation for deep neural networks through multi-task learning
Huang, Z., Li, J., Siniscalchi, S. M., Chen, I.-F., Wu, J., and Lee, C.-H · 2015
Earlier work this paper cites.
Speaker adaptive training of deep neural network acoustic models using i-vectors
Miao, Y., Zhang, H., and Metze, F · 2015
Earlier work this paper cites.
Cluster adaptive training for deep neural network
Tan, T., Qian, Y., Yin, M., Zhuang, Y., and Yu, K · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
Earlier work this paper cites.
Machine learning with adversaries: Byzantine tolerant gradient descent
Blanchard, P., El Mhamdi, E., Guerraoui, R., and Stainer, J · 2017
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Practical secure aggregation for privacy-preserving machine learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K · 2017
Earlier work this paper cites.
Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Chen, Y., Su, L., and Xu, J · 2017
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J. 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., and Agüera y Arcas, B · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
Earlier work this paper cites.
Federated multi-task learning
Smith, V., Chiang, C.-K., Sanjabi, M., and Talwalkar, A · 2017
Earlier work this paper cites.
Machine learning models that remember too much
Song, C., Ristenpart, T., and Shmatikov, V · 2017
Cited alongside, same era.
Recent progresses in deep learning based acoustic models
Yu, D. and Li, J · 2017
Cited alongside, same era.
How to backdoor federated learning
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., and Shmatikov, V · 2018
Cited alongside, same era.
Personalized and private peer-to-peer machine learning
Bellet, A., Guerraoui, R., Taziki, M., and Tommasi, M · 2018
Cited alongside, same era.
The hidden vulnerability of distributed learning in Byzantium
El Mhamdi, E., Guerraoui, R., and Rouault, S · 2018
Cited alongside, same era.
How to start training: The effect of initialization and architecture
Hanin, B. and Rolnick, D · 2018
Adversarial initialization–when your network performs the way I want
Grosse, K., Trost, T. A., Mosbach, M., Backes, M., and Klakow, D · 2019
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Guha, N., Talwalkar, A., and Smith, V · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Hsu, T.-M. H., Qi, H., and Brown, M · 2019
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Improving federated learning personalization via model agnostic meta learning
Jiang, Y., Konečnỳ, J., Rush, K., and Kannan, S · 2019
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Cited alongside, same era.
Federated learning for mobile keyboard prediction
Hard, A., Rao, K., Mathews, R., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D · 2018
Cited alongside, same era.
Learning differentially private recurrent language models
McMahan, H. B., Ramage, D., Talwar, K., and Zhang, L · 2018
Cited alongside, same era.
Byzantine-robust distributed learning: Towards optimal statistical rates
Yin, D., Chen, Y., Ramchandran, K., and Bartlett, P · 2018
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Bagdasaryan, E., Poursaeed, O., and Shmatikov, V · 2019
Cited alongside, same era.
Analyzing federated learning through an adversarial lens
Bhagoji, A. N., Chakraborty, S., Mittal, P., and Calo, S · 2019
Cited alongside, same era.
Towards federated learning at scale: System design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konecny, J., Mazzocchi, S., McMahan, H. B., Van Overveldt, T., Petrou, D., Ramage, D., and Roselander, J · 2019
Cited alongside, same era.
Kairouz, P. et al · 2019
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Exploiting unintended feature leakage in collaborative learning
Melis, L., Song, C., De Cristofaro, E., and Shmatikov, V · 2019
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Private federated learning with domain adaptation
Peterson, D. W., Kanani, P., and Marathe, V. J · 2019
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At I/O ’19: Building a more helpful Google for everyone
Pichai, S · 2019
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PyTorch examples
pytorch · 2019
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Detox: A redundancy-based framework for faster and more robust gradient aggregation
Rajput, S., Wang, H., Charles, Z., and Papailiopoulos, D · 2019
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Reddit comments
Reddit · 2019
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Federated evaluation of on-device personalization
Wang, K., Mathews, R., Kiddon, C., Eichner, H., Beaufays, F., and Ramage, D · 2019
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Bayesian nonparametric federated learning of neural networks
Yurochkin, M., Agarwal, M., Ghosh, S., Greenewald, K., Hoang, T. N., and Khazaeni, Y · 2019
Later among the works it cites.
Personalized federated learning with Moreau envelopes
Dinh, C. T., Tran, N. H., and Nguyen, T. D · 2020
Closest in time.
Personalized federated learning: A meta-learning approach
Fallah, A., Mokhtari, A., and Ozdaglar, A · 2020
Closest in time.