Bicheng Ying

Converge Faster, Talk Less: Hessian-Informed Federated Zeroth-Order Optimization

Breaking Memory and Communication Barriers in Model-Parallel Fine-Tuning of Large Language Models

Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable

Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization

FAST: Federated Average with Snapshot Unleashes Arbitrary Client Participation