56 citations · 97 across the 11 of their papers we have counts for
16 papers
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…
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…