3 papers
cs.AI2026
From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
Xinghao Wu, Jianwei Niu, Guogang Zhu +3
Heterogeneous federated learning (HtFL) aims to enable collaboration among clients that differ in both data distributions and model architectures. Prototype-based methods, which co…
cs.LG2026
Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning
Xinghao Wu, Jianwei Niu, Xuefeng Liu +4
Federated Prototype Learning (FedPL) has emerged as an effective strategy for handling data heterogeneity in Federated Learning (FL). In FedPL, clients collaboratively construct a…
cs.LG2025
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations
Guogang Zhu, Xuefeng Liu, Jianwei Niu +3
In personalized federated learning (PFL), it is widely recognized that achieving both high model generalization and effective personalization poses a significant challenge due to t…