16 citations · 53 across the 14 of their papers we have counts for
3 papers · 1 filter
SuperSFL: Resource-Heterogeneous Federated Split Learning with Weight-Sharing Super-Networks
Abdullah Al Asif, Sixing Yu, Juan Pablo Munoz +2
SplitFed Learning (SFL) combines federated learning and split learning to enable collaborative training across distributed edge devices; however, it faces significant challenges in…
Towards Seamless Management of AI Models in High-Performance Computing
Sixing Yu, Murali Emani, Chunhua Liao +4
With the increasing prevalence of artificial intelligence (AI) in diverse science/engineering communities, AI models emerge on an unprecedented scale among various domains. However…
Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion
Duy Phuong Nguyen, Sixing Yu, J. Pablo Muñoz +1
Concerned with user data privacy, this paper presents a new federated learning (FL) method that trains machine learning models on edge devices without accessing sensitive data. Tra…