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.

翻译：多无线边缘设备间的协同推理具有显著增强人工智能（AI）应用的潜力，尤其在感知与计算机视觉领域。该方法通常包含三阶段流程：a) 通过感知进行数据采集，b) 特征提取，c) 用于传输的特征编码。然而，传输提取的特征会带来显著的隐私风险，因为敏感个人数据可能在此过程中暴露。为应对这一挑战，我们提出一种新颖的隐私保护协同推理机制，其中网络中的每个边缘设备在将提取的特征传输至中央服务器进行推理前，会确保其隐私安全。我们的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，我们专门针对分类任务提出了一种空中汇聚方案，该方案为传输特征的隐私性提供形式化保证，并为分类准确率建立了下界。