420 citations · 1.3k across the 62 of their papers we have counts for
73 papers
EMO: Edge Model Overlays to Scale Model Size in Federated Learning
Di Wu, Weibo He, Wanglei Feng +3
Federated Learning (FL) trains machine learning models on edge devices with distributed data. However, the computational and memory limitations of these devices restrict the traini…
DNNShifter: An Efficient DNN Pruning System for Edge Computing
Bailey J. Eccles, Philip Rodgers, Peter Kilpatrick +2
Deep neural networks (DNNs) underpin many machine learning applications. Production quality DNN models achieve high inference accuracy by training millions of DNN parameters which…
EcoFed: Efficient Communication for DNN Partitioning-based Federated Learning
Di Wu, Rehmat Ullah, Philip Rodgers +3
Efficiently running federated learning (FL) on resource-constrained devices is challenging since they are required to train computationally intensive deep neural networks (DNN) ind…
PiPar: Pipeline Parallelism for Collaborative Machine Learning
Zihan Zhang, Philip Rodgers, Peter Kilpatrick +2
Collaborative machine learning (CML) techniques, such as federated learning, have been proposed to train deep learning models across multiple mobile devices and a server. CML techn…
AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions
Sundas Iftikhar, Sukhpal Singh Gill, Chenghao Song +15
Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature o…
ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy
JunKyu Lee, Blesson Varghese, Hans Vandierendonck
This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy v…