56 citations · 167 across the 11 of their papers we have counts for
10 papers · 1 filter
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…
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…
Are Clouds Ready to Accelerate Ad hoc Financial Simulations?
Blesson Varghese, Adam Barker
Applications employed in the financial services industry to capture and estimate a variety of risk metrics are underpinned by stochastic simulations which are data, memory and comp…