249 citations · 338 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…
Time Series Segmentation through Automatic Feature Learning
Wei-Han Lee, Jorge Ortiz, Bongjun Ko +1
Internet of things (IoT) applications have become increasingly popular in recent years, with applications ranging from building energy monitoring to personal health tracking and ac…