52 citations · 55 across the 6 of their papers we have counts for
6 papers
Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning
Jing Liu, Chenxuanyin Zou, Jiayang Ren +5
Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obsta…
PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMs
Jing Liu, Kun Yang, Yan Wang +5
Agentic AI systems are reshaping communications and networking by deploying autonomous intelligent agents capable of collaborative learning while maintaining data privacy at networ…
Diffusion-Guided Semantic Consistency for Multimodal Heterogeneity
Jing Liu, Zhengliang Guo, Yan Wang +4
Federated learning (FL) is severely challenged by non-independent and identically distributed (non-IID) client data, a problem that degrades global model performance, especially in…
Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey
Jing Liu, Yao Du, Kun Yang +8
Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources…
A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systems
Linxiao Gong, Hao Yang, Gaoyun Fang +7
The explosive growth of video data has driven the development of distributed video analytics in cloud-edge-terminal collaborative (CETC) systems, enabling efficient video processin…
Privacy-Preserving Video Anomaly Detection: A Survey
Yang Liu, Siao Liu, Xiaoguang Zhu +7
Video Anomaly Detection (VAD) aims to automatically analyze spatiotemporal patterns in surveillance videos collected from open spaces to detect anomalous events that may cause harm…