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تسجيل مستخدم جديدAge-Optimal Power Allocation in Industrial IoT: A Risk-Sensitive Federated Learning Approach
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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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While information delivery in industrial Internet of things demands reliability and latency guarantees, the freshness of the controllers available information, measured by the age of information (AoI), is paramount for high-performing industrial auto
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