2 papers
cs.AI2026
Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance
Shiqiang Wang, Herbert Woisetschläger, Hans Arno Jacobsen +1
Data is fundamental to large language models (LLMs). However, understanding of what makes certain data useful for different stages of an LLM workflow, including training, tuning, a…
cs.LG2025
Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization
Jake B. Perazzone, Shiqiang Wang, Mingyue Ji +1
Federated learning (FL) is a useful tool that enables the training of machine learning models over distributed data without having to collect data centrally. When deploying FL in c…