3 citations · 8 across the 5 of their papers we have counts for
6 papers
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…
Performance Weighting for Robust Federated Learning Against Corrupted Sources
Dimitris Stripelis, Marcin Abram, Jose Luis Ambite
Federated Learning has emerged as a dominant computational paradigm for distributed machine learning. Its unique data privacy properties allow us to collaboratively train models wh…
Federated Named Entity Recognition
Joel Mathew, Dimitris Stripelis, José Luis Ambite
We present an analysis of the performance of Federated Learning in a paradigmatic natural-language processing task: Named-Entity Recognition (NER). For our evaluation, we use the l…
Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption
Dimitris Stripelis, Hamza Saleem, Tanmay Ghai +8
Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to…
Scaling Neuroscience Research using Federated Learning
Dimitris Stripelis, Jose Luis Ambite, Pradeep Lam +1
The amount of biomedical data continues to grow rapidly. However, the ability to analyze these data is limited due to privacy and regulatory concerns. Machine learning approaches t…
Accelerating Federated Learning in Heterogeneous Data and Computational Environments
Dimitris Stripelis, Jose Luis Ambite
There are situations where data relevant to a machine learning problem are distributed among multiple locations that cannot share the data due to regulatory, competitiveness, or pr…