24 citations · 28 across the 8 of their papers we have counts for
12 papers
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…
CONTINUER: Maintaining Distributed DNN Services During Edge Failures
Ayesha Abdul Majeed, Peter Kilpatrick, Ivor Spence +1
Partitioning and deploying Deep Neural Networks (DNNs) across edge nodes may be used to meet performance objectives of applications. However, the failure of a single node may resul…
FedFly: Towards Migration in Edge-based Distributed Federated Learning
Rehmat Ullah, Di Wu, Paul Harvey +3
Federated learning (FL) is a privacy-preserving distributed machine learning technique that trains models while keeping all the original data generated on devices locally. Since de…
FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning
Di Wu, Rehmat Ullah, Paul Harvey +3
Applying Federated Learning (FL) on Internet-of-Things devices is necessitated by the large volumes of data they produce and growing concerns of data privacy. However, there are th…
NEUKONFIG: Reducing Edge Service Downtime When Repartitioning DNNs
Ayesha Abdul Majeed, Peter Kilpatrick, Ivor Spence +1
Deep Neural Networks (DNNs) may be partitioned across the edge and the cloud to improve the performance efficiency of inference. DNN partitions are determined based on operational…
A Case For Adaptive Deep Neural Networks in Edge Computing
Francis McNamee, Schahram Dustadar, Peter Kilpatrick +3
Edge computing offers an additional layer of compute infrastructure closer to the data source before raw data from privacy-sensitive and performance-critical applications is transf…
Modelling Fog Offloading Performance
Ayesha Abdul Majeed, Peter Kilpatrick, Ivor Spence +1
Fog computing has emerged as a computing paradigm aimed at addressing the issues of latency, bandwidth and privacy when mobile devices are communicating with remote cloud services.…
Performance Estimation of Container-Based Cloud-to-Fog Offloading
Ayesha Abdul Majeed, Peter Kilpatrick, Ivor Spence +1
Fog computing offloads latency critical application services running on the Cloud in close proximity to end-user devices onto resources located at the edge of the network. The rese…
Iso-Quality of Service: Fairly Ranking Servers for Real-Time Data Analytics
Giorgis Georgakoudis, Charles J. Gillan, Ahmed Sayed +3
We present a mathematically rigorous Quality-of-Service (QoS) metric which relates the achievable quality of service metric (QoS) for a real-time analytics service to the server en…
Methods and Metrics for Fair Server Assessment under Real-Time Financial Workloads
Giorgis Georgakoudis, Charles J. Gillan, Ahmed Sayed +3
Energy efficiency has been a daunting challenge for datacenters. The financial industry operates some of the largest datacenters in the world. With increasing energy costs and the…