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Secure federated learning is a privacy-preserving framework to improve machine learning models by training over large volumes of data collected by mobile users.
W. Diffie and M. Hellman, “New directions in cryptography,” IEEE Trans. on Inf. Theory , vol. 22, no. 6, pp. 644–654, 1976
1976
Earlier work this paper cites.
A. Shamir, “How to share a secret,” Communications of the ACM , vol. 22, no. 11, pp. 612–613, 1979
1979
Earlier work this paper cites.
D. Dolev and H. R. Strong, “Authenticated algorithms for Byzantine agreement,” SIAM Journal on Computing , vol. 12, no. 4, pp. 656–666, 1983
1983
Earlier work this paper cites.
P. Feldman, “A practical scheme for non-interactive verifiable secret sharing,” in 28th Annual Symposium on Foundations of Computer Science . IEEE, 1987, pp. 427–438
1987
Earlier work this paper cites.
L. Bottou, “Online learning and stochastic approximations,” Online learning in neural networks , vol. 17, no. 9, p. 142, 1998
1998
Earlier work this paper cites.
S. Gao, “A new algorithm for decoding reed-solomon codes,” in Communications, information and network security . Springer, 2003, pp. 55–68
2003
Earlier work this paper cites.
G. F. Coulouris, J. Dollimore, and T. Kindberg, Distributed systems: concepts and design . pearson education, 2005
2005
Earlier work this paper cites.
C. Dwork, “Differential privacy: A survey of results,” in International conference on theory and applications of models of computation . Springer, 2008, pp. 1–19
2008
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Citeseer, Tech. Rep., 2009
2009
Earlier work this paper cites.
F. Kerschbaum, D. Biswas, and S. de Hoogh, “Performance comparison of secure comparison protocols,” in 2009 20th International Workshop on Database and Expert Systems Application . IEEE, 2009, pp. 133–136
2009
Earlier work this paper cites.
Y. LeCun, C. Cortes, and C. Burges, “MNIST handwritten digit database,” [Online]. Available: http://yann. lecun. com/exdb/mnist , vol. 2, 2010
2010
Earlier work this paper cites.
G. Ács and C. Castelluccia, “I have a dream! (differentially private smart metering),” in International Workshop on Information Hiding . Springer, 2011, pp. 118–132
2011
Earlier work this paper cites.
2015
Earlier work this paper cites.
J. Konečný, H. B. McMahan, F. X. Yu, P. Richtarik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” in Conference on Neural Information Processing Systems: Workshop on Private Multi-Party Machine Learning , 2016
2016
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics , ser. Proceedings of Machine Learning Research, vol. 54, Fort Lauderdale, FL, USA, Apr 2017, pp. 1273–1282
2017
Earlier work this paper cites.
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy-preserving machine learning,” in ACM SIGSAC Conf. on Comp. and Comm. Security . ACM, 2017, pp. 1175–1191
2017
Cited alongside, same era.
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Int. Conf. on Artificial Int. and Stat. (AISTATS) , 2017, pp. 1273–1282
2017
Cited alongside, same era.
P. Blanchard, E. M. E. Mhamdi, R. Guerraoui, and J. Stainer, “Machine learning with adversaries: Byzantine tolerant gradient descent,” in Advances in Neural Information Processing Systems , 2017, pp. 119–129
2017
Cited alongside, same era.
Y. Chen, L. Su, and J. Xu, “Distributed statistical machine learning in adversarial settings: Byzantine gradient descent,” Proceedings of the ACM on Measurement and Analysis of Computing Systems , vol. 1, no. 2, pp. 1–25, 2017
Z. Wang, M. Song, Z. Zhang, Y. Song, Q. Wang, and H. Qi, “Beyond inferring class representatives: User-level privacy leakage from federated learning,” in IEEE Conference on Computer Communications (INFOCOM’2019) , 2019, pp. 2512–2520
2019
Later among the works it cites.
L. Li, W. Xu, T. Chen, G. B. Giannakis, and Q. Ling, “Rsa: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 1544–1551
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
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2017
Cited alongside, same era.
2017
Cited alongside, same era.
D. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnovic, “Qsgd: Communication-efficient sgd via gradient quantization and encoding,” in Advances in Neural Information Processing Systems , 2017, pp. 1709–1720
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Cited alongside, same era.
D. Yin, Y. Chen, R. Kannan, and P. Bartlett, “Byzantine-robust distributed learning: Towards optimal statistical rates,” in Proceedings of the 35th International Conference on Machine Learning , vol. 80, Stockholm Sweden, 10–15 Jul 2018, pp. 5650–5659
2018
Cited alongside, same era.
D. Alistarh, Z. Allen-Zhu, and J. Li, “Byzantine stochastic gradient descent,” in Advances in Neural Information Processing Systems , 2018, pp. 4613–4623
2018
Cited alongside, same era.
2018
Cited alongside, same era.
K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konecny, S. Mazzocchi, H. B. McMahan et al. , “Towards federated learning at scale: System design,” in 2nd SysML Conf. , 2019
2019
Cited alongside, same era.
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
J. So, B. Güler, and A. S. Avestimehr, “Byzantine-resilient secure federated learning,” IEEE Journal on Selected Areas in Communications , 2020
2020
Closest in time.
2020
Closest in time.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine , vol. 37, no. 3, pp. 50–60, 2020
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
J. Bell, K. Bonawitz, A. Gascón, T. Lepoint, and M. Raykova, “Secure single-server aggregation with (poly)logarithmic overhead,” IACR Cryptol. ePrint Arch. , 2020. [Online]. Available: https://eprint.iacr.org/2020/704
2020
Closest in time.