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

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.