3 papers
cs.LG2026
In-Context Reward Adaptation for Robust Preference Modeling
Zhenyu Sun, Zheng Xu, Ermin Wei
Reinforcement Learning from Human Feedback (RLHF) typically relies on static reward models to align Large Language Models with human preferences. However, human values are inherent…
cs.LG2026
Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning
Baris Askin, Holger R. Roth, Zhenyu Sun +3
Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, yet its scalability is limited by synchronization overhead. Asynch…
cs.LG2024
Debiasing Federated Learning with Correlated Client Participation
Zhenyu Sun, Ziyang Zhang, Zheng Xu +3
In cross-device federated learning (FL) with millions of mobile clients, only a small subset of clients participate in training in every communication round, and Federated Averagin…