3 papers
cs.LG2025
Harmonizing Generalization and Personalization in Ring-topology Decentralized Federated Learning
Shunxin Guo, Jiaqi Lv, Xin Geng
We introduce Ring-topology Decentralized Federated Learning (RDFL) for distributed model training, aiming to avoid the inherent risks of centralized failure in server-based FL. How…
cs.DC2025
GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning
Shunxin Guo, Jiaqi Lv, Qiufeng Wang +1
Real-world \underline{F}ederated \underline{L}earning systems often encounter \underline{D}ynamic clients with \underline{A}gnostic and highly heterogeneous data distributions (DAF…
cs.LG2025
STHFL: Spatio-Temporal Heterogeneous Federated Learning
Shunxin Guo, Hongsong Wang, Shuxia Lin +2
Federated learning is a new framework that protects data privacy and allows multiple devices to cooperate in training machine learning models. Previous studies have proposed multip…