1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
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…
DMP_AI: An AI-Aided K-12 System for Teaching and Learning in Diverse Schools
Zhen-Qun Yang, Jiannong Cao, Xiaoyin Li +4
The use of Artificial Intelligence (AI) has gained momentum in education. However, the use of AI in K-12 education is still in its nascent stages, and further research and developm…
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…