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This paper deals with distributed finite-sum optimization for learning over networks in the presence of malicious Byzantine attacks.
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P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer, “Machine learning with adversaries: Byzantine tolerant gradient descent,” Proceedings of NIPS , Long Beach, California, USA, Dec. 2017
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D. Yin, Y. Chen, K. Ramchandran, and P. Bartlett, “Byzantine-robust distributed learning: Towards optimal statistical rates,” Proceedings of ICML , Stockholm, Sweden, Jul. 2018
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Later among the works it cites.
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2018
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2018
Later among the works it cites.
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2017
Cited alongside, same era.
M. W. Schmidt, N. Le Roux, and F. R. Bach, “Minimizing finite sums with the stochastic average gradient,” Mathematical Programming , vol. 162, no. 1–2, pp. 83–112, Mar. 2017
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L. Zhou, K. Yeh, G. Hancke, Z. Liu, and C. Su, “Security and privacy for the industrial Internet of Things: An overview of approaches to safeguard endpoints,” IEEE Signal Processing Magazine , vol. 35, no. 5, pp. 76–87, Sep. 2018
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Y. Chen, S. Kar, and J. M. F. Moura, “The Internet of Things: Secure distributed inference,” IEEE Signal Processing Magazine , vol. 35, no. 5, pp. 64–75, Sep. 2018
2018
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H. Tang, X. Lian, M. Yan, C. Zhang, and J. Liu, “D2: Decentralized training over decentralized data,” Proceedings of ICML , Stockholm, Sweden, Jul. 2018
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2019
Closest in time.
Y. Chen, L. Su, and J. Xu, “Distributed statistical machine learning in adversarial settings: Byzantine gradient descent,” Proceedings of SIGMETRICS , Phonenix, Arizona, USA, Jun. 2019
2019
Closest in time.
L. Li, W. Xu, T. Chen, G. B. Giannakis, and Q. Ling, “RSA: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets,” Proceedings of AAAI , Honolulu, Hawaii, USA, Jan. 2019
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
F. Lin, Q. Ling, and Z. Xiong, “Byzantine-resilient distributed large-scale matrix completion,” Proceedings of ICASSP , Brighton, UK, May 2019
2019
Closest in time.