1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.RO2026
RoboFL: Federated Expert Assembly for World Action Models
Rongyu Zhang, Ruizhi Fan, Yunfan Lou +8
Vision-language-action and world-action models are increasingly popular, yet remain bottlenecked by physical interaction data that is scarce, institutionally siloed, and task-heter…
cs.LG2024
FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning
Boyu Fan, Chenrui Wu, Xiang Su +1
Despite extensive research into data heterogeneity in federated learning (FL), system heterogeneity remains a significant yet often overlooked challenge. Traditional FL approaches…
cs.AI2024★ 1 cited
Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data
Rongyu Zhang, Yun Chen, Chenrui Wu +2
Federated learning (FL) offers a privacy-centric distributed learning framework, enabling model training on individual clients and central aggregation without necessitating data ex…