3 papers
cs.LG2025
Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence
Matin Ansaripour, Shayan Talaei, Giorgi Nadiradze +1
Distributed optimization is the standard way of speeding up machine learning training, and most of the research in the area focuses on distributed first-order, gradient-based metho…
cs.DC2025
Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging
Shigang Li, Tal Ben-Nun, Giorgi Nadiradze +4
Deep learning at scale is dominated by communication time. Distributing samples across nodes usually yields the best performance, but poses scaling challenges due to global informa…
cs.LG2025
Communication-Efficient Federated Learning With Data and Client Heterogeneity
Hossein Zakerinia, Shayan Talaei, Giorgi Nadiradze +1
Federated Learning (FL) enables large-scale distributed training of machine learning models, while still allowing individual nodes to maintain data locally. However, executing FL a…