Collaborative Inference over Wireless Channels with Feature Differential Privacy

Understand

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.