Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks. (arXiv:2202.11098v1 [cs.LG])

Deep-learning-based intelligent services have become prevalent in
cyber-physical applications including smart cities and health-care.
Collaborative end-edge-cloud computing for deep learning provides a range of
performance and efficiency that can address application requirements through
computation offloading. The decision to offload computation is a
communication-computation co-optimization problem that varies with both system
parameters (e.g., network condition) and workload characteristics (e.g.,
inputs). Identifying optimal orchestration considering the cross-layer
opportunities and requirements in the face of varying system dynamics is a
challenging multi-dimensional problem. …

arxiv cloud deep learning deep learning inference edge hybrid learning networks