Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints

TL;DR

This paper introduces a novel communication-efficient framework for distributed sensor networks that dynamically manages thresholds to control false negative rates while respecting communication limits, with proven guarantees and demonstrated effectiveness.

Contribution

The paper proposes the CD-CRC framework, combining online gradient descent and conformal risk control to optimize decision thresholds under communication constraints in sensor networks.

Findings

Offers deterministic worst-case guarantees on FNR and communication overhead.

Provides regret bounds for false positive rate performance.

Simulation results confirm effectiveness in resource-constrained environments.

Abstract

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.