Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT
This work addresses reliability and latency challenges for industrial automation systems, but it is incremental as it builds on existing federated learning and optimization methods.
The paper tackles minimizing sensor transmit power in industrial IoT while meeting age-of-information and latency constraints by modeling latency tail behavior with a generalized Pareto distribution and using Lyapunov optimization, and it proposes a correlation-aware federated learning approach for local model selection, showing superiority over a baseline.
While information delivery in industrial Internet of things demands reliability and latency guarantees, the freshness of the controller's available information, measured by the age of information (AoI), is paramount for high-performing industrial automation. The problem in this work is cast as a sensor's transmit power minimization subject to the peak-AoI requirement and a probabilistic constraint on queuing latency. We further characterize the tail behavior of the latency by a generalized Pareto distribution (GPD) for solving the power allocation problem through Lyapunov optimization. As each sensor utilizes its own data to locally train the GPD model, we incorporate federated learning and propose a local-model selection approach which accounts for correlation among the sensor's training data. Numerical results show the tradeoff between the transmit power, peak AoI, and delay's tail distribution. Furthermore, we verify the superiority of the proposed correlation-aware approach for selecting the local models in federated learning over an existing baseline.