Dynamic Split Computing Framework for Multi-Task Learning Models: A Deep Reinforcement Learning Approach

Dynamic Split Computing Framework for Multi-Task Learning Models: A Deep Reinforcement Learning Approach

Split computing has emerged as a promising approach to alleviate the resource constraints of IoT devices by offloading computation to edge servers. However, conventional split computing schemes fail to effectively support multi-task learning (MTL) models, which feature a shared backbone and multiple...

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Bibliographic Details
Main Authors: Haneul Ko, Sangwon Seo, Sangheon Pack
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Split computing
inference
multi-task learning
reinforcement learning
deep deterministic policy gradient
Online Access:https://ieeexplore.ieee.org/document/11029019/
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