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Cooperative DNN partitioning for accelerating DNN-empowered disease diagnosis via swarm reinforcement learning.
- Source :
- Applied Soft Computing; Nov2023, Vol. 148, pN.PAG-N.PAG, 1p
- Publication Year :
- 2023
-
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. • We present a cooperative DNN partitioning system in end-edge cooperation networks. • We propose a swarm reinforcement learning algorithm to support the proposed system. • Simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15684946
- Volume :
- 148
- Database :
- Supplemental Index
- Journal :
- Applied Soft Computing
- Publication Type :
- Academic Journal
- Accession number :
- 173707234
- Full Text :
- https://doi.org/10.1016/j.asoc.2023.110844