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LEMON: Lossless model expansion
Our empirical results demonstrate that LEMON reduces computational costs by 56. 7% for Vision Transformers and 33. 2% for BERT when compared to training from scratch.
Joint Coreset Construction and Quantization for Distributed Machine Learning
Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication and computation costs.
Communication-efficient k-Means for Edge-based Machine Learning
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 learning computation to nearby edge servers.
Sharing Models or Coresets: A Study based on Membership Inference Attack
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 approaches have been proposed: collecting and aggregating local models (federated learning) and collecting and training over representative data summaries (coreset).
Robust Coreset Construction for Distributed Machine Learning
Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over distributed data.
