1 citations · 1 across the 4 of their papers we have counts for
5 papers
Understanding Trajectory Heterogeneity in Federated World Model Learning
Yipan Wei, Zhaokun Yan, Ziming Hong +2
World models learn state evolution from trajectories, making access to temporal context a central training requirement. Federated learning can use distributed records, while owners…
How Implicit Bias Accumulates and Propagates in LLM Long-term Memory
Yiming Ma, Lixu Wang, Lionel Z. Wang +6
Long-term memory mechanisms enable Large Language Models (LLMs) to maintain continuity and personalization across extended interaction lifecycles, but they also introduce new and u…
FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning
Yuan Yao, Lixu Wang, Jiaqi Wu +7
Federated learning (FL) enables collaborative training across clients while preserving privacy. While most existing FL methods assume homogeneous model architectures, client hetero…
A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks
Jiaqi Wu, Jing Liu, Yang Liu +6
The proliferation of Internet of things (IoT) devices in smart cities, transportation, healthcare, and industrial applications, coupled with the explosive growth of AI-driven servi…
Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment
Jiaqi Wu, Shihao Zhang, Simin Chen +7
Edge computing has emerged as a key paradigm for deploying deep learning-based object detection in time-sensitive scenarios. However, existing edge detection methods face challenge…