2 papers
cs.LG2026
Unifying Local Communications and Local Updates for LLM Pretraining
Pietro Cagnasso, Eugene Belilovsky, Edouard Oyallon
Communication-efficient pre-training of LLMs is increasingly important as training draws on compute distributed across clusters, data centers, and lower-bandwidth links. Many pract…
cs.AI2025
Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning
Debora Caldarola, Pietro Cagnasso, Barbara Caputo +1
Federated learning (FL) enables collaborative model training with privacy preservation. Data heterogeneity across edge devices (clients) can cause models to converge to sharp minim…