2 papers
cs.LG2026
Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation
M Yashwanth, Arunabh Singh, Ashok Nayak +2
Federated Learning (FL) algorithms implicitly assume that clients passively comply with server-side orchestration by sharing local model updates upon server request. However, this…
cs.LG2025
Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining
Daouda Sow, Herbert Woisetschläger, Saikiran Bulusu +3
Pretraining large language models (LLMs) on vast and heterogeneous datasets is crucial for achieving state-of-the-art performance across diverse downstream tasks. However, current…