18 citations · 35 across the 10 of their papers we have counts for
4 papers · 1 filter
Joint Coreset Construction and Quantization for Distributed Machine Learning
Hanlin Lu, Changchang Liu, Shiqiang Wang +4
Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication…
Communication-efficient k-Means for Edge-based Machine Learning
Hanlin Lu, Ting He, Shiqiang Wang +5
We consider the problem of computing the k-means centers for a large high-dimensional dataset in the context of edge-based machine learning, where data sources offload machine lear…
Sharing Models or Coresets: A Study based on Membership Inference Attack
Hanlin Lu, Changchang Liu, Ting He +2
Distributed machine learning generally aims at training a global model based on distributed data without collecting all the data to a centralized location, where two different appr…
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…