Age-optimal power allocation in industrial IoT : a risk-sensitive federated learning approach
Hsu, Yung-Lin; Liu, Chen-Feng; Samarakoon, Sumudu; Wei, Hung-Yu; Bennis, Mehdi (2021-10-22)
Y. -L. Hsu, C. -F. Liu, S. Samarakoon, H. -Y. Wei and M. Bennis, "Age-Optimal Power Allocation in Industrial IoT: A Risk-Sensitive Federated Learning Approach," 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2021, pp. 1323-1328, doi: 10.1109/PIMRC50174.2021.9569536
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https://urn.fi/URN:NBN:fi-fe2022022821135
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Abstract
This work studies a real-time environment monitoring scenario in the industrial Internet of things, where wireless sensors proactively collect environmental data and transmit it to the controller. We adopt the notion of risk-sensitivity in financial mathematics as the objective to jointly minimize the mean, variance, and other higher-order statistics of the network energy consumption subject to the constraints on the age of information (AoI) threshold violation probability and the AoI exceedances over a pre-defined threshold. We characterize the extreme AoI staleness using results in extreme value theory and propose a distributed power allocation approach by weaving in together principles of Lyapunov optimization and federated learning (FL). Simulation results demonstrate that the proposed FL-based distributed solution is on par with the centralized baseline while consuming 28.50% less system energy and outperforms the other baselines.
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