56 citations · 167 across the 11 of their papers we have counts for
11 papers
FedComm: Understanding Communication Protocols for Edge-based Federated Learning
Gary Cleland, Di Wu, Rehmat Ullah +1
Federated learning (FL) trains machine learning (ML) models on devices using locally generated data and exchanges models without transferring raw data to a distant server. This exc…
Feasibility of Fog Computing
Blesson Varghese, Nan Wang, Dimitrios S. Nikolopoulos +1
As billions of devices get connected to the Internet, it will not be sustainable to use the cloud as a centralised server. The way forward is to decentralise computations away from…
Challenges and Opportunities in Edge Computing
Blesson Varghese, Nan Wang, Sakil Barbhuiya +2
Many cloud-based applications employ a data centre as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model pla…
A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook Shootings
Nan Wang, Blesson Varghese, Peter D. Donnelly
Gun related violence is a complex issue and accounts for a large proportion of violent incidents. In the research reported in this paper, we set out to investigate the pro-gun and…
Cloud Benchmarking For Maximising Performance of Scientific Applications
Blesson Varghese, Ozgur Akgun, Ian Miguel +2
How can applications be deployed on the cloud to achieve maximum performance? This question is challenging to address with the availability of a wide variety of cloud Virtual Machi…
The GPU vs Phi Debate: Risk Analytics Using Many-Core Computing
Blesson Varghese
The risk of reinsurance portfolios covering globally occurring natural catastrophes, such as earthquakes and hurricanes, is quantified by employing simulations. These simulations a…