PPTADI: A privacy-preserving training and accelerated distributed inference framework in low-resource AIoT scenarios

Artificial intelligence of things (AIoT) is a technology that combines AI and IoT, which realizes the interconnection and gives them more intelligent features between devices. However, there are some challenges in the data process. In this paper, we propose a new framework called PPTADI to train AI models while preserving privacy and to accelerate the inference process. Experiments show that PPTADI can effectively prevent label leakage, gradient attacks and model inversion attacks compared to the conventional split federated learning frameworks. In the meanwhile, PPTADI reduces the total inference delay by up to 35 % and the transmission delay by up to 65 % comparing with some SOTA schemes for distributed inference.