24 citations · 28 across the 8 of their papers we have counts for
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cs.DC2021
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…
cs.DC2021★ 3 cited
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…
cs.DC2021
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…