Age-Optimal Power Allocation in Industrial IoT: A Risk-Sensitive Federated Learning Approach

Yung-Lin Hsu; Chen-Feng Liu; Sumudu Samarakoon; Hung-Yu Wei; Mehdi Bennis

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arXiv · 2012.06860

Age-Optimal Power Allocation in Industrial IoT: A Risk-Sensitive Federated Learning Approach

Yung-Lin Hsu·Chen-Feng Liu·Sumudu Samarakoon·Hung-Yu Wei·Mehdi Bennis

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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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Yung-Lin Hsu, Chen-Feng Liu, Sumudu Samarakoon, Hung-Yu Wei, Mehdi Bennis. 2020-12-12. Age-Optimal Power Allocation in Industrial IoT: A Risk-Sensitive Federated Learning Approach. https://arxiv.org/abs/2012.06860

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