16 citations · 53 across the 14 of their papers we have counts for
7 papers · 1 filter
PerfMamba: Performance Analysis and Pruning of Selective State Space Models
Abdullah Al Asif, Mobina Kashaniyan, Sixing Yu +2
Recent advances in sequence modeling have introduced selective SSMs as promising alternatives to Transformer architectures, offering theoretical computational efficiency and sequen…
The Landscape and Challenges of HPC Research and LLMs
Le Chen, Nesreen K. Ahmed, Akash Dutta +14
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning. Both encoder-decoder models and prompt-based techniques ha…
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning
Sixing Yu, J. Pablo Muñoz, Ali Jannesari
Federated learning (FL) offers privacy-preserving decentralized machine learning, optimizing models at edge clients without sharing private data. Simultaneously, foundation models…
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models
Sixing Yu, J. Pablo Muñoz, Ali Jannesari
Foundation Models (FMs), such as LLaMA, BERT, GPT, ViT, and CLIP, have demonstrated remarkable success in a wide range of applications, driven by their ability to leverage vast amo…
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search
Sixing Yu, J. Pablo Muñoz, Ali Jannesari
Federated Learning (FL) is extensively used to train AI/ML models in distributed and privacy-preserving settings. Participant edge devices in FL systems typically contain non-indep…
SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning
Sixing Yu, Phuong Nguyen, Waqwoya Abebe +3
Federated learning~(FL) facilitates the training and deploying AI models on edge devices. Preserving user data privacy in FL introduces several challenges, including expensive comm…
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient
Sixing Yu, Phuong Nguyen, Ali Anwar +1
Federated Learning (FL) has emerged as a new paradigm for training machine learning models distributively without sacrificing data security and privacy. Learning models on edge dev…