59 citations · 68 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
Revisiting the Arguments for Edge Computing Research
Blesson Varghese, Eyal de Lara, Aaron Ding +8
This article argues that low latency, high bandwidth, device proliferation, sustainable digital infrastructure, and data privacy and sovereignty continue to motivate the need for e…
Multiparty Session Types for Safe Runtime Adaptation in an Actor Language (Extended version)
Paul Harvey, Simon Fowler, Ornela Dardha +1
Human fallibility, unpredictable operating environments, and the heterogeneity of hardware devices are driving the need for software to be able to adapt as seen in the Internet of…