3 papers
cs.DC2025
Resource Heterogeneity-Aware and Utilization-Enhanced Scheduling for Deep Learning Clusters
Abeda Sultana, Nabin Pakka, Fei Xu +3
Scheduling deep learning (DL) models to train on powerful clusters with accelerators like GPUs and TPUs, presently falls short, either lacking fine-grained heterogeneity awareness…
cs.DC2025
SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training
Md Sirajul Islam, Sanjeev Panta, Fei Xu +3
Federated Learning (FL) is a promising distributed machine learning framework that allows collaborative learning of a global model across decentralized devices without uploading th…
cs.DC2024
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
Md Sirajul Islam, Simin Javaherian, Fei Xu +3
Federated learning (FL) is an emerging distributed machine learning paradigm that enables collaborative training of machine learning models over decentralized devices without expos…