1 citations · 1 across the 7 of their papers we have counts for
8 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…
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…
Threshold Signatures for Central Bank Digital Currencies
Mostafa Abdelrahman, Filip Rezabek, Lars Hupel +2
Digital signatures are crucial for securing Central Bank Digital Currencies (CBDCs) transactions. Like most forms of digital currencies, CBDC solutions rely on signatures for trans…
Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods
Tianlun Hu, Qi Liao, Qiang Liu +2
Network slicing is a key technique in 5G and beyond for efficiently supporting diverse services. Many network slicing solutions rely on deep learning to manage complex and high-dim…
Multilayer Environment and Toolchain for Holistic NetwOrk Design and Analysis
Filip Rezabek, Kilian Glas, Richard von Seck +3
The recent developments and research in distributed ledger technologies and blockchain have contributed to the increasing adoption of distributed systems. To collect relevant insig…
Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis
Wenxuan Ye, Chendi Qian, Xueli An +2
Integrating native AI support into the network architecture is an essential objective of 6G. Federated Learning (FL) emerges as a potential paradigm, facilitating decentralized AI…