Secure and Efficient Model Training Framework for Multiuser Semantic Communications via Over-the-Air Mixup

This paper presents SIMix, a training framework for systems where multiple users learn AI models together over wireless networks while protecting their private data. The system uses Over-the-Air Mixup (OAM, a technique that combines data from multiple users through wireless transmission to hide sensitive information) and groups users strategically to reduce communication needs by up to 25% while defending against model inversion attacks (attempts to reconstruct private training data from a trained model) and label inference attacks (guessing what category a user's data belongs to).