When HPC Scheduling Meets Active Learning: Maximizing The Performance with Minimal Data

Staleness Aware Semi-asynchronous Federated Learning

Preserving Near-Optimal Gradient Sparsification Cost for Scalable Distributed Deep Learning

MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training

DEFT: Exploiting Gradient Norm Difference between Model Layers for Scalable Gradient Sparsification

Dynamic Programming-Based Multilevel Graph Partitioning for Large-Scale Graph Data

Conditional LSTM-VAE-based Data Augmentation for Disaster Classification Prediction

Addressing Client Heterogeneity in Synchronous Federated Learning: The CHAFL Approach

A low redundancy and high time efficiency large-scale task assignment strategy for heterogeneous service-oriented cloud computing systems

WAVE: designing a heuristics-based three-way breadth-first search on GPUs