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.
View on arXiv@article{zhu2025_2409.07902,
title={ Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints },
author={ Meiyi Zhu and Matteo Zecchin and Sangwoo Park and Caili Guo and Chunyan Feng and Petar Popovski and Osvaldo Simeone },
journal={arXiv preprint arXiv:2409.07902},
year={ 2025 }
}