JOINT POWER COST AND LATENCY MINIMIZATION FOR SECURE COLLABORATIVE LEARNING SYSTEM
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DOI:
https://doi.org/10.15625/1813-9663/38/3/17094Keywords:
Federated learning, Covert communication, Latency minimization, Trustfulness, Auction.Abstract
This work investigates the model update security in a collaborative learning or federated learning network by using the covert communication. The CC uses the jamming signal and multiple friendly jammers (FJs) are deployed that can offer jamming services to the model owner, i.e., a base station (BS). To enable the BS to select the best FJ, i.e., the lowest cost FJ, a truthful auction is adopted. Then, a problem is formulated to optimize the jamming power, transmission power, and local accuracy. The objective is to minimize the training latency, subject to the security performance requirement and budget of the BS. To solve the non-convex problem, we adopt a Successive Convex Approximation algorithm. The simulation results reveals some interesting things. For example, the trustful auction reduces the jamming cost of the BS as the number of FJs increases.
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References
J. Tan, Y.-C. Liang, N. C. Luong, and D. Niyato, “Toward smart security
enhancement of federated learning networks,” IEEE Network, vol. 35,
no. 1, pp. 340–347, Jan./Feb. 2020.
Z. Yang, M. Chen, W. Saad, C. S. Hong, and M. Shikh-Bahaei, “Energy
efficient federated learning over wireless communication networks,” IEEE
Transactions on Wireless Communications, vol. 20, no. 3, pp. 1935–1949,
Mar. 2020.
N. T. T. Van, N. C. Luong, H. T. Nguyen, F. Shaohan, D. Niyato, and D. I.
Kim, “Latency minimization in covert communication-enabled federated
learning network,” IEEE Transactions on Vehicular Technology, vol. 70,
no. 12, pp. 13 447–13 452, 2021.
DOI: https://doi.org/10.1007/s12592-021-00397-y
N. C. Luong, Z. Xiong, P. Wang, and D. Niyato, “Optimal auction for
edge computing resource management in mobile blockchain networks:
A deep learning approach,” in 2018 IEEE international conference on
communications (ICC). IEEE, 2018, pp. 1–6.
N. C. Luong, P. Wang, D. Niyato, Y. Wen, and Z. Han, “Resource
management in cloud networking using economic analysis and pricing
models: A survey,” IEEE Communications Surveys & Tutorials, vol. 19,
no. 2, pp. 954–1001, 2017.
Z. Yang, M. Chen, W. Saad, C. S. Hong, and M. Shikh-Bahaei, “Energy
efficient federated learning over wireless communication networks,” IEEE
Transactions on Wireless Communications, 2020
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