4 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2025
TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction
Weijie Liu, Ziwei Zhan, Carlee Joe-Wong +5
Non-independent and identically distributed (Non-IID) data across edge clients have long posed significant challenges to federated learning (FL) training in edge computing environm…
cs.DC2025★ 1 cited
Analytic Personalized Federated Meta-Learning
Shunxian Gu, Chaoqun You, Deke Guo +4
Analytic Federated Learning (AFL) is an enhanced gradient-free federated learning (FL) paradigm designed to accelerate training by updating the global model in a single step with c…
cs.LG2024★ 4 cited
FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
Ziwei Zhan, Wenkuan Zhao, Yuanqing Li +6
Federated learning (FL) is a collaborative machine learning approach that enables multiple clients to train models without sharing their private data. With the rise of deep learnin…