Loss-Adapter: Addressing Network Packet Loss in Distributed Inference for Lossy IoT Environments

The deployment of deep neural networks (DNNs) in the Internet of Things (IoT) is essential for various AI applications. This trend highlights the growing need for distributed inference (DI) capabilities to process data efficiently and effectively in IoT environments. In lossy networks, data loss can significantly affect inference accuracy. Prioritizing algorithmic optimizations over hardware modifications proves to be a more effective approach for enhancing DNN performance in the IoT. We propose a plug-and-play module called Loss-Adapter, which aims to improve the accuracy of DI on lossy networks. To simulate network packet loss, we design a Gaussian distribution sampling dropout. This method is utilized during the training of the Loss-Adapter to acquire fault-tolerant inference capabilities for operation over unreliable communication links. Moreover, we employ the tree-structured Parzen estimator algorithm to optimize the parameters of the Gaussian dropout layer. We design two IoT packet loss simulation systems to conduct experiments, exploring the impact of the DNN split position and packet size on inference accuracy. Our experiments demonstrate that applying the Loss-Adapter significantly enhances the inference accuracy of DNN models operating on lossy IoT networks.