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While information delivery in industrial Internet of things demandsreliability and latency guarantees, the freshness of the controller's availableinformation, measured by the age of information (AoI), is paramount forhigh-performing industrial automation. The problem in this work is cast as asensor's transmit power minimization subject to the peak-AoI requirement and aprobabilistic constraint on queuing latency. We further characterize the tailbehavior of the latency by a generalized Pareto distribution (GPD) for solvingthe power allocation problem through Lyapunov optimization. As each sensorutilizes its own data to locally train the GPD model, we incorporate federatedlearning and propose a local-model selection approach which accounts forcorrelation among the sensor's training data. Numerical results show thetradeoff between the transmit power, peak AoI, and delay's tail distribution.Furthermore, we verify the superiority of the proposed correlation-awareapproach for selecting the local models in federated learning over an existingbaseline.

Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT