在通信受限下，动态调整阈值以控制误检率，提升传感器网络可靠性。
Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints
- 结合在线梯度与置信区间控制，动态调节局部与全局判断阈值
- 保证误检率不高于目标值，通信开销可控且有理论上限
- 适合资源受限的分布式传感场景，尤其对高可靠性要求系统
本文提出一种通信受限下的分布式置信风险控制（CD-CRC）框架，用于传感器网络中的多标签分类问题（如分割）。该框架通过在线指数梯度下降估计各传感器观测质量，并利用在线置信风险控制机制动态调整本地与全局阈值，确保目标误报率（FNR）达标，同时满足通信容量限制。理论证明，该方法在误报率和通信开销方面具有确定性最坏情况保证；在假阳性率（FPR）上的遗憾性能则由关键超参数决定。仿真结果表明，该方法在通信资源受限环境下表现优异，显著提升了分布式传感器网络的性能与可靠性。
原文摘要 · Abstract (English)
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.
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