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

Summary

Researchers propose SIMix, a training framework for multiuser semantic communication that combines Over-the-Air Mixup (OAM) with label-aware user grouping. The OAM mixes users' semantic features over wireless channels to obfuscate sensitive data and cut communication overhead, while an extended max-clique algorithm partitions users to reduce model inversion attack success. On CIFAR-10 and Tiny ImageNet, the method reduces communication overhead by up to 25%, reaches 17.58 dB PSNR under inversion attack (a 20.98 dB reduction), and lowers label inference attack success by 13.44%.