1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.DC2024★ 1 cited
ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency
Yuhang Yao, Han Jin, Alay Dilipbhai Shah +7
Large language models (LLMs) have surged in popularity and are extensively used in commercial applications, where the efficiency of model serving is crucial for the user experience…
cs.LG2023
Federated Learning over Harmonized Data Silos
Dimitris Stripelis, Jose Luis Ambite
Federated Learning is a distributed machine learning approach that enables geographically distributed data silos to collaboratively learn a joint machine learning model without sha…
cs.LG2022
Towards Sparsified Federated Neuroimaging Models via Weight Pruning
Dimitris Stripelis, Umang Gupta, Nikhil Dhinagar +3
Federated training of large deep neural networks can often be restrictive due to the increasing costs of communicating the updates with increasing model sizes. Various model prunin…