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

• URL: http://arxiv.org/abs/2409.07902v2
• Date: Wed, 29 Jan 2025 16:24:56 GMT
• Title: Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints
• Authors: Meiyi Zhu, Matteo Zecchin, Sangwoo Park, Caili Guo, Chunyan Feng, Petar Popovski, Osvaldo Simeone,
• Abstract summary: Communication-constrained distributed conformal risk control (CD-CRC) CD-CRC is a novel decision-making framework for sensor networks under communication constraints.
• Score: 61.62410595953275
• License:
• 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.

Related papers

• Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks [55.467288506826755]
  Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks. Most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance. We propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme.
  arXiv  Detail & Related papers  (2025-01-20T04:26:21Z)
• EGEAN: An Exposure-Guided Embedding Alignment Network for Post-Click Conversion Estimation [6.178133899988549]
  Post-click conversion rate (CVR) estimation is crucial for online advertising systems. Despite advances in causal approaches, CVR estimation still faces challenges due to Covariate Shift. This study proposes an Exposure-Guided Embedding Alignment Network (EGEAN) to address this problem.
  arXiv  Detail & Related papers  (2024-12-08T10:17:02Z)
• Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks [60.54852710216738]
  We introduce a novel digital twin-assisted optimization framework, called D-REC, to ensure reliable caching in nextG wireless networks. By incorporating reliability modules into a constrained decision process, D-REC can adaptively adjust actions, rewards, and states to comply with advantageous constraints.
  arXiv  Detail & Related papers  (2024-06-29T02:40:28Z)
• Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel Noises [18.539501941328393]
  This paper develops a latent diffusion model-enabled SemCom system to handle outliers in source data. A lightweight single-layer latent space transformation adapter completes one-shot learning at the transmitter. An end-to-end consistency distillation strategy is used to distill the diffusion models trained in latent space.
  arXiv  Detail & Related papers  (2024-06-09T23:39:31Z)
• Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control [63.310780486820796]
  We show how a parameterization of recurrent connectivity influences robustness in closed-loop settings. We find that closed-form continuous-time neural networks (CfCs) with fewer parameters can outperform their full-rank, fully-connected counterparts.
  arXiv  Detail & Related papers  (2023-10-05T21:44:18Z)
• Perimeter Control with Heterogeneous Metering Rates for Cordon Signals: A Physics-Regularized Multi-Agent Reinforcement Learning Approach [12.86346901414289]
  Perimeter Control (PC) strategies have been proposed to address urban road network control in oversaturated situations. This paper leverages a Multi-Agent Reinforcement Learning (MARL)-based traffic signal control framework to decompose this PC problem. A physics regularization approach for the MARL framework is proposed to ensure the distributed cordon signal controllers are aware of the global network state.
  arXiv  Detail & Related papers  (2023-08-24T13:51:16Z)
• Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning Framework for Congestion Control in Tactical Environments [53.08686495706487]
  This paper proposes an RL framework that leverages an accurate and parallelizable emulation environment to reenact the conditions of a tactical network. We evaluate our RL learning framework by training a MARLIN agent in conditions replicating a bottleneck link transition between a Satellite Communication (SATCOM) and an UHF Wide Band (UHF) radio link.
  arXiv  Detail & Related papers  (2023-06-27T16:15:15Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• Cross-Layered Distributed Data-driven Framework For Enhanced Smart Grid Cyber-Physical Security [3.8237485961848128]
  Cross-Layer Ensemble CorrDet with Adaptive Statistics is presented. It integrates the detection of faulty SG measurement data as well as inconsistent network inter-arrival times and transmission delays. Results show that CECD-AS can detect multiple False Data Injections, Denial of Service (DoS) and Man In The Middle (MITM) attacks with a high F1-score.
  arXiv  Detail & Related papers  (2021-11-10T00:00:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.