3 papers
cs.LG2026
FedRD: Reducing Divergences for Generalized Federated Learning via Heterogeneity-aware Parameter Guidance
Kaile Wang, Jiannong Cao, Yu Yang +2
Heterogeneous federated learning (HFL) aims to ensure effective and privacy-preserving collaboration among different entities. As newly joined clients require significant adjustmen…
cs.LG2026
FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices
Kaile Wang, Jiannong Cao, Yu Yang +2
With the rapid development of the Internet of Things (IoT), AI model training on private data such as human sensing data is highly desired. Federated learning (FL) has emerged as a…
cs.LG2025
FedAli: Personalized Federated Learning Alignment with Prototype Layers for Generalized Mobile Services
Sannara Ek, Kaile Wang, François Portet +2
Personalized Federated Learning (PFL) enables distributed training on edge devices, allowing models to collaboratively learn global patterns while tailoring their parameters to bet…