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20232026
most citedUnderstanding Generalization of Federated Learning via Stability: Heterogeneity Matters

3 citations · 6 across the 7 of their papers we have counts for

collaborators

7 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.LG2025

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…

cs.LG2024

A Stochastic Quasi-Newton Method for Non-convex Optimization with Non-uniform Smoothness

Zhenyu Sun, Ermin Wei

Classical convergence analyses for optimization algorithms rely on the widely-adopted uniform smoothness assumption. However, recent experimental studies have demonstrated that man…

cs.GT2023

Incentivized Federated Learning and Unlearning

Ningning Ding, Zhenyu Sun, Ermin Wei +1

To protect users' right to be forgotten in federated learning, federated unlearning aims at eliminating the impact of leaving users' data on the global learned model. The current r…

cs.LG2023★ 3 cited

Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters

Zhenyu Sun, Xiaochun Niu, Ermin Wei

Generalization performance is a key metric in evaluating machine learning models when applied to real-world applications. Good generalization indicates the model can predict unseen…

cond-mat.mes-hall2023★ 3 cited

Machine-Learning-Assisted and Real-Time-Feedback-Controlled Growth of InAs/GaAs Quantum Dots

Chao Shen, Wenkang Zhan, Kaiyao Xin +11

Self-assembled InAs/GaAs quantum dots (QDs) have properties highly valuable for developing various optoelectronic devices such as QD lasers and single photon sources. The applicati…