1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization
Shunxin Guo, Hongsong Wang, Shuxia Lin +2
Federated learning is an efficient framework designed to facilitate collaborative model training across multiple distributed devices while preserving user data privacy. A significa…
Dynamic Heterogeneous Federated Learning with Multi-Level Prototypes
Shunxin Guo, Hongsong Wang, Xin Geng
Federated learning shows promise as a privacy-preserving collaborative learning technique. Existing heterogeneous federated learning mainly focuses on skewing the label distributio…