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Federated Learning enables one to jointly train a machine learning model across distributed clients holding sensitive datasets.
SCAFFOLD: stochastic controlled averaging for on-device federated learning
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., and Suresh, A. T · 1910
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How to share a secret
Rivest, R. L., Shamir, A., and Tauman, Y · 1979
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How to generate and exchange secrets (extended abstract)
Yao, A. C · 1986
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How to play any mental game or a completeness theorem for protocols with honest majority
Goldreich, O., Micali, S., and Wigderson, A · 1987
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Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
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1-bit stochastic gradient descent and application to data-parallel distributed training of speech dnns
Seide, F., Fu, H., Droppo, J., Li, G., and Yu, D · 2014
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Secure Multiparty Computation and Secret Sharing
Cramer, R., Damgård, I., and Nielsen, J. B · 2015
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Privacy-preserving deep learning
Shokri, R. and Shmatikov, V · 2015
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Scalable distributed DNN training using commodity GPU cloud computing
Strom, N · 2015
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Federated learning of deep networks using model averaging
McMahan, H. B., Moore, E., Ramage, D., and y Arcas, B. A · 2016
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A survey on homomorphic encryption schemes: Theory and implementation
Acar, A., Aksu, H., Uluagac, A. S., and Conti, M · 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
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2017
Cited alongside, same era.
How to simulate it - A tutorial on the simulation proof technique
Lindell, Y · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Cited alongside, same era.
Terngrad: Ternary gradients to reduce communication in distributed deep learning
Wen, W., Xu, C., Yan, F., Wu, C., Wang, Y., Chen, Y., and Li, H · 2017
Cited alongside, same era.
Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning
Sparsified SGD with memory
Stich, S. U., Cordonnier, J., and Jaggi, M · 2018
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Towards federated learning at scale: System design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konecný, J., Mazzocchi, S., McMahan, B., Overveldt, T. V., Petrou, D., Ramage, D., and Roselander, J · 2019
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Error feedback fixes signsgd and other gradient compression schemes
Karimireddy, S. P., Rebjock, Q., Stich, S. U., and Jaggi, M · 2019
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Federated learning in mobile edge networks: A comprehensive survey
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y., Yang, Q., Niyato, D., and Miao, C · 2019
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Exploiting unintended feature leakage in collaborative learning
Melis, L., Song, C., Cristofaro, E. D., and Shmatikov, V · 2019
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Zhang, C., Li, S., Xia, J., Wang, W., Yan, F., and Liu, Y · 2017
Cited alongside, same era.
SIGNSGD: compressed optimisation for non-convex problems
Bernstein, J., Wang, Y., Azizzadenesheli, K., and Anandkumar, A · 2018
Cited alongside, same era.
Protection against reconstruction and its applications in private federated learning
Bhowmick, A., Duchi, J. C., Freudiger, J., Kapoor, G., and Rogers, R · 2018
Cited alongside, same era.
The secret sharer: Measuring unintended neural network memorization & extracting secrets
Carlini, N., Liu, C., Kos, J., Erlingsson, Ú., and Song, D. X · 2018
Cited alongside, same era.
A Pragmatic Introduction to Secure Multi-Party Computation
Evans, D., Kolesnikov, V., and Rosulek, M · 2018
Cited alongside, same era.
On the convergence of federated optimization in heterogeneous networks
Sahu, A. K., Li, T., Sanjabi, M., Zaheer, M., Talwalkar, A., and Smith, V · 2018
Cited alongside, same era.
Sparse binary compression: Towards distributed deep learning with minimal communication
Sattler, F., Wiedemann, S., Müller, K., and Samek, W · 2018
Cited alongside, same era.
Doublesqueeze: Parallel stochastic gradient descent with double-pass error-compensated compression
Tang, H., Yu, C., Lian, X., Zhang, T., and Liu, J · 2019
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A hybrid approach to privacy-preserving federated learning - (extended abstract)
Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., and Zhou, Y · 2019
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Hybridalpha: An efficient approach for privacy-preserving federated learning
Xu, R., Baracaldo, N., Zhou, Y., Anwar, A., and Ludwig, H · 2019
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Secure single-server aggregation with (poly)logarithmic overhead
Bell, J. H., Bonawitz, K. A., Gascón, A., Lepoint, T., and Raykova, M · 2020
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High performance logistic regression for privacy-preserving genome analysis
Cock, M. D., Dowsley, R., Nascimento, A. C. A., Railsback, D., Shen, J., and Todoki, A · 2020
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Eastfly:efficient and secure ternary federated learning
Dong, Y., Chen, X., Shen, L., and Wang, D · 2020
Closest in time.
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V · 2020
Closest in time.