A Secure Federated Learning Framework for 5G Networks

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2016

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K. Fan, Y. Ren, Y. Wang, H. Li, and Y. Yang, “Blockchain-based efficient privacy preserving and data sharing scheme of content-centric network in 5g,” IET Communications , vol. 12, no. 5, pp. 527–532, 2017

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R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in 2017 IEEE Symposium on Security and Privacy (SP) , May 2017, pp. 3–18

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R. C. Geyer, T. Klein, and M. Nabi, “Differentially private federated learning: A client level perspective,” arXiv preprint arXiv:1712.07557 , 2017

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2017

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J. Cosmas, B. Meunier, K. Ali, N. Jawad, M. Salih, Y. Zhang, Z. Hadad, B. Globen, H. Gokmen, S. Malkos, M. Cakan, H. Koumaras, A. Kourtis, C. Sakkas, D. Negru, M. Lacaud, M. Ran, E. Ran, J. Garcia, W. Li, L. Huang, R. Zetik, K. Cabaj, W. Mazurczyk, X. Zhang, and A. Kapovits, “A 5g radio-light sdn architecture for wireless and mobile network access in buildings,” in 2018 IEEE 5G World Forum (5GWF) , July 2018, pp. 135–140

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K. Cabaj, M. Gregorczyk, W. Mazurczyk, P. Nowakowski, and P. Żórawski, “Sdn-based mitigation of scanning attacks for the 5g internet of radio light system,” in Proceedings of the 13th International Conference on Availability, Reliability and Security , ser. ARES 2018. New York, NY, USA: ACM, 2018, pp. 49:1–49:10. [Online]. Available: http://doi.acm.org/10.1145/3230833.3233248

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A. B. Kurtulmus and K. Daniel, “Trustless machine learning contracts; evaluating and exchanging machine learning models on the ethereum blockchain,” arXiv preprint arXiv:1802.10185 , 2018

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Y. Wu, Y. Liu, S. H. Ahmed, J. Peng, and A. A. A. El-Latif, “Dominant dataset selection algorithms for electricity consumption time-series data analysis based on affine transformation,” IEEE Internet of Things Journal , pp. 1–1, 2019

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C. Xu, J. Ren, D. Zhang, Y. Zhang, Z. Qin, and K. Ren, “Ganobfuscator: Mitigating information leakage under gan via differential privacy,” IEEE Transactions on Information Forensics and Security , vol. 14, no. 9, pp. 2358–2371, Sep. 2019

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T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” arXiv preprint arXiv:1908.07873 , 2019

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2019

Later among the works it cites.

K. Cabaj, M. Gregorczyk, W. Mazurczyk, P. Nowakowski, and P. Żórawski, “Network threats mitigation using software-defined networking for the 5g internet of radio light system,” Security and Communication Networks , vol. 2019, in press, 2019

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