提出隐私保护的联邦隐因子模型，修复无线传感器缺失数据。
Federated Latent Factor Learning for Recovering Wireless Sensor Networks Signal with Privacy-Preserving
- 传感器本地训练，仅上传梯度保护隐私
- 同区域传感器共享隐向量，提升恢复精度
- 适合注重隐私与数据安全的传感场景
无线传感器网络（WSNs）是智能感知领域的前沿方向。由于传感器故障和节能策略，采集数据常存在大量缺失，影响后续分析与决策。尽管隐因子学习（LFL）在填补缺失数据方面表现有效，但缺乏对数据隐私的充分保护。为此，本文创新性提出基于联邦隐因子学习（FLFL）的空间信号恢复（SSR）模型——FLFL-SSR。其核心思想包括：1）设计传感器级联邦学习框架，各传感器仅上传梯度更新而非原始数据以优化全局模型；2）提出本地空间共享策略，允许同一空间区域内的传感器共享其隐特征向量，捕捉空间相关性，提升恢复准确率。在两个真实世界WSN数据集上的实验表明，所提模型在恢复性能上优于现有联邦方法。
原文摘要 · Abstract (English)
Wireless Sensor Networks (WSNs) are a cutting-edge domain in the field of intelligent sensing. Due to sensor failures and energy-saving strategies, the collected data often have massive missing data, hindering subsequent analysis and decision-making. Although Latent Factor Learning (LFL) has been proven effective in recovering missing data, it fails to sufficiently consider data privacy protection. To address this issue, this paper innovatively proposes a federated latent factor learning (FLFL) based spatial signal recovery (SSR) model, named FLFL-SSR. Its main idea is two-fold: 1) it designs a sensor-level federated learning framework, where each sensor uploads only gradient updates instead of raw data to optimize the global model, and 2) it proposes a local spatial sharing strategy, allowing sensors within the same spatial region to share their latent feature vectors, capturing spatial correlations and enhancing recovery accuracy. Experimental results on two real-world WSNs datasets demonstrate that the proposed model outperforms existing federated methods in terms of recovery performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。