2 citations · 2 across the 1 of their papers we have counts for
4 papers
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning
Jianyi Zhang, Hao Frank Yang, Ang Li +5
Previous studies on federated learning (FL) often encounter performance degradation due to data heterogeneity among different clients. In light of the recent advances in multimodal…
Federated Large Language Models: Current Progress and Future Directions
Yuhang Yao, Jianyi Zhang, Junda Wu +11
Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy…
AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents
Yiyi Lu, Hoi Ian Au, Junyao Zhang +8
Electronic Design Automation (EDA) remains heavily reliant on tool command language (Tcl) scripting to drive complex RTL-to-GDSII flows. This scripting-based paradigm is labor-inte…
Federated Unsupervised Visual Representation Learning via Exploiting General Content and Personal Style
Yuewei Yang, Jingwei Sun, Ang Li +2
Discriminative unsupervised learning methods such as contrastive learning have demonstrated the ability to learn generalized visual representations on centralized data. It is nonet…