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

TL;DR

This paper addresses optimizing power and latency in industrial IoT by integrating federated learning with correlation-aware model selection to improve age of information and tail latency guarantees.

Contribution

It introduces a novel correlation-aware federated learning approach for local model selection, enhancing latency tail behavior and power efficiency in industrial IoT.

Findings

Correlation-aware model selection improves latency tail performance.

Proposed method reduces transmit power while maintaining AoI constraints.

Numerical results demonstrate tradeoffs and superiority over baseline methods.

Abstract

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.