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Found 5 papers, 4 papers with code
DNNShifter: An Efficient DNN Pruning System for Edge Computing
Compared to sparse models, the pruned model variants are up to 5. 14x smaller and have a 1. 67x inference latency speedup, with no compromise to sparse model accuracy.
CONTINUER: Maintaining Distributed DNN Services During Edge Failures
This paper will leverage trade-offs in accuracy, end-to-end latency and downtime for selecting the best technique given user-defined objectives (accuracy, latency and downtime thresholds) when an edge node fails.
FedFly: Towards Migration in Edge-based Distributed Federated Learning
Our empirical results on the CIFAR10 dataset, with both balanced and imbalanced data distribution, support our claims that FedFly can reduce training time by up to 33% when a device moves after 50% of the training is completed, and by up to 45% when 90% of the training is completed when compared to state-of-the-art offloading approach in FL.
FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning
Further, FedAdapt adopts reinforcement learning based optimization and clustering to adaptively identify which layers of the DNN should be offloaded for each individual device on to a server to tackle the challenges of computational heterogeneity and changing network bandwidth.
A Case For Adaptive Deep Neural Networks in Edge Computing
However, there is limited understanding of: (a) whether and how evolving operational conditions (increased CPU and memory utilization at the edge or reduced data transfer rates between the edge and the cloud) affect the performance of already deployed DNNs, and (b) whether a new partition configuration is required to maximize performance.
