Cooperative DNN partitioning for accelerating DNN-empowered disease diagnosis via swarm reinforcement learning

Abstract

As the most promising machine learning technology, deep neural networks (DNNs) have garnered significant attention in the field of disease diagnosis, i.e., DNN-empowered disease diagnosis. DNN-empowered disease diagnostic models with complex neural network structures are generally computation-intensive and latency-sensitive. To reduce latency, the partitioning and offloading of DNN-empowered disease diagnostic models in edge computing networks has emerged as a potential solution. However, DNN partitioning still faces two key challenges: the limitation of edge resources and the dynamic of network environments. To address these challenges, we propose a cooperative DNN partitioning system aimed at accelerating the processing of the model in multi-access edge computing networks. Specifically, we model the cooperative DNN partitioning offloading problem as a multi-agent Markov decision process with the goal of minimizing the long-term service latency (i.e., accelerating DNN-empowered disease diagnosis). To tackle this optimization problem, we further propose a swarm reinforcement learning (SRL) algorithm. Each agent of the proposed SRL can learn from local data and generate a judicious offloading action independently. Numerous simulation results show that the proposed SRL outperforms existing offloading algorithms and can significantly accelerate DNN-empowered disease diagnosis.