249 citations · 338 across the 6 of their papers we have counts for
8 papers
Overcoming Noisy and Irrelevant Data in Federated Learning
Tiffany Tuor, Shiqiang Wang, Bong Jun Ko +2
Many image and vision applications require a large amount of data for model training. Collecting all such data at a central location can be challenging due to data privacy and comm…
Model Pruning Enables Efficient Federated Learning on Edge Devices
Yuang Jiang, Shiqiang Wang, Victor Valls +4
Federated learning (FL) allows model training from local data collected by edge/mobile devices while preserving data privacy, which has wide applicability to image and vision appli…
Online Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems
Tiffany Tuor, Shiqiang Wang, Kin K. Leung +1
Large-scale distributed computing systems often contain thousands of distributed nodes (machines). Monitoring the conditions of these nodes is important for system management purpo…
SECRET: Semantically Enhanced Classification of Real-world Tasks
Ayten Ozge Akmandor, Jorge Ortiz, Irene Manotas +2
Supervised machine learning (ML) algorithms are aimed at maximizing classification performance under available energy and storage constraints. They try to map the training data to…
Harvesting Time-Series Data from Service-Based Systems Hosted in MANETs
Petr Novotny, Bong Jun Ko, Alexander L. Wolf
We are concerned with reliably harvesting data collected from service-based systems hosted on a mobile ad hoc network (MANET). More specifically, we are concerned with time-bounded…
Experimentation With Fuzzy Interest Forwarding in Named Data Networking
Spyridon Mastorakis, Kevin Chan, Bongjun Ko +2
In the current Named Data Networking implementation, forwarding a data request requires finding an exact match between the prefix of the name carried in the request and a forwardin…