5 citations · 5 across the 7 of their papers we have counts for
5 papers · 1 filter
FedSceneX: Time-to-Target Orchestration for Same-Scene Multimodal Federated Edge Learning
Dhe Yeong Tchalla, Beining Wu, Jun Huang +2
Federated learning at the sensing edge is typically evaluated by communication rounds, yet a round does not represent a fixed amount of work. Even on identical hardware, the method…
When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks
Zihao Ding, Jun Huang, Liang Dong
Self-improving federated agent networks keep training after deployment by collecting new trajectories with the current policy and feeding them back into later rounds. This closed l…
RELIEF: Turning Missing Modalities into Training Acceleration for Federated Learning on Heterogeneous IoT Edge
Beining Wu, Zihao Ding, Jun Huang
Federated learning (FL) over heterogeneous IoT edge devices faces coupled system-modality-data heterogeneity: the lower-cost device carries both fewer sensors and less computationa…
"X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems
Beining Wu, Jun Huang, Shui Yu
The development of next-generation networking systems has inherently shifted from throughput-based paradigms towards intelligent, information-aware designs that emphasize the quali…
Enhancing Vehicular Platooning with Wireless Federated Learning: A Resource-Aware Control Framework
Beining Wu, Jun Huang, Qiang Duan +2
This paper aims to enhance the performance of Vehicular Platooning (VP) systems integrated with Wireless Federated Learning (WFL). In highly dynamic environments, vehicular platoon…