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This work studies a real-time environment monitoring scenario in theindustrial Internet of things, where wireless sensors proactively collectenvironmental data and transmit it to the controller. We adopt the notion ofrisk-sensitivity in financial mathematics as the objective to jointly minimizethe mean, variance, and other higher-order statistics of the network energyconsumption subject to the constraints on the age of information (AoI)threshold violation probability and the AoI exceedances over a pre-definedthreshold. We characterize the extreme AoI staleness using results in extremevalue theory and propose a distributed power allocation approach by weaving intogether principles of Lyapunov optimization and federated learning (FL).Simulation results demonstrate that the proposed FL-based distributed solutionis on par with the centralized baseline while consuming 28.50% less systemenergy and outperforms the other baselines.

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