无线边缘设备协作推理时保护特征隐私，降低通信开销。
Collaborative Inference over Wireless Channels with Feature Differential Privacy
- 设备提取特征后差分加密再传输，保障隐私
- 提出空中聚合方案，保证分类准确率下限
- 适合隐私敏感的智能传感与视觉应用
多个无线边缘设备间的协同推理有望显著提升人工智能应用，尤其在感知和计算机视觉领域。该过程通常包含三阶段：数据采集、特征提取与特征编码传输。然而，传输提取特征存在重大隐私风险，可能暴露敏感个人信息。为此，我们提出一种新型隐私保护协同推理机制：网络中每个边缘设备在将特征传输至中心服务器进行推理前，均对提取的特征进行隐私保护处理。该方法旨在实现两个主要目标：1）降低通信开销；2）在特征传输过程中确保严格的隐私保障，同时保持有效的推理性能。此外，我们设计了一种专用于分类任务的过-air池化方案，为传输特征提供形式化隐私保障，并建立分类准确率的下界。
原文摘要 · Abstract (English)
Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications, particularly for sensing and computer vision. This approach typically involves a three-stage process: a) data acquisition through sensing, b) feature extraction, and c) feature encoding for transmission. However, transmitting the extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process. To address this challenge, we propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference. Our approach is designed to achieve two primary objectives: 1) reducing communication overhead and 2) ensuring strict privacy guarantees during feature transmission, while maintaining effective inference performance. Additionally, we introduce an over-the-air pooling scheme specifically designed for classification tasks, which provides formal guarantees on the privacy of transmitted features and establishes a lower bound on classification accuracy.
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