56 citations · 97 across the 11 of their papers we have counts for
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cs.DC2023
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…
cs.DC2023★ 1 cited
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…