5 papers
FedCC: Towards Addressing Label Distribution Skews in Distillation-Based Federated Learning
Wenxuan Ye, Onur Ayan, Xueli An +1
Federated Learning (FL) enables distributed clients to collaboratively train models without sharing raw data, making it promising for leveraging massive devices in communication ne…
Select to Think: Unlocking SLM Potential with Local Sufficiency
Wenxuan Ye, Yangyang Zhang, Xueli An +2
Small language models (SLMs) offer efficient deployment, yet they often lag behind their larger counterparts (LLMs) in reasoning. Existing remedies either invoke an LLM at points o…
STARE-VLA: Progressive Stage-Aware Reinforcement for Fine-Tuning Vision-Language-Action Models
Feng Xu, Guangyao Zhai, Xin Kong +4
Recent advances in Vision-Language-Action (VLA) models, powered by large language models and reinforcement learning-based fine-tuning, have shown remarkable progress in robotic man…
Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models
Wenxuan Ye, Xueli An, Onur Ayan +3
Large models, renowned for superior performance, outperform smaller ones even without billion-parameter scales. While mobile network servers have ample computational resources to s…
FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View
Wenxuan Ye, Xueli An, Junfan Wang +2
Native AI support is a key objective in the evolution of 6G networks, with Federated Learning (FL) emerging as a promising paradigm. FL allows decentralized clients to collaborativ…