Fault-Tolerant Collaborative Inference through the Edge-PRUNE Framework. (arXiv:2206.08152v1 [cs.LG])

Web: http://arxiv.org/abs/2206.08152

Collaborative inference has received significant research interest in machine
learning as a vehicle for distributing computation load, reducing latency, as
well as addressing privacy preservation in communications. Recent collaborative
inference frameworks have adopted dynamic inference methodologies such as
early-exit and run-time partitioning of neural networks. However, as machine
learning frameworks scale in the number of inference inputs, e.g., in
surveillance applications, fault tolerance related to device failure needs to
be considered. This paper presents the Edge-PRUNE distributed computing
framework, built …

arxiv collaborative edge framework inference lg