3 papers
cs.LG2026
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…
cs.LG2025
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…
cs.NI2025
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…