Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints
This work addresses reliability and communication efficiency for distributed sensor networks, representing an incremental improvement over existing conformal risk control methods.
The paper tackles the problem of multi-label classification in sensor networks under communication constraints by proposing the CD-CRC framework, which dynamically adjusts thresholds to ensure a target false negative rate while adhering to communication limits, with simulation results showing its effectiveness in resource-constrained environments.
This paper presents communication-constrained distributed conformal risk control (CD-CRC) framework, a novel decision-making framework for sensor networks under communication constraints. Targeting multi-label classification problems, such as segmentation, CD-CRC dynamically adjusts local and global thresholds used to identify significant labels with the goal of ensuring a target false negative rate (FNR), while adhering to communication capacity limits. CD-CRC builds on online exponentiated gradient descent to estimate the relative quality of the observations of different sensors, and on online conformal risk control (CRC) as a mechanism to control local and global thresholds. CD-CRC is proved to offer deterministic worst-case performance guarantees in terms of FNR and communication overhead, while the regret performance in terms of false positive rate (FPR) is characterized as a function of the key hyperparameters. Simulation results highlight the effectiveness of CD-CRC, particularly in communication resource-constrained environments, making it a valuable tool for enhancing the performance and reliability of distributed sensor networks.