2 citations · 2 across the 1 of their papers we have counts for
21 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…
DA-Cramming: Enhancing Cost-Effective Language Model Pretraining with Dependency Agreement Integration
Martin Kuo, Jianyi Zhang, Dongting Li +1
Pretraining language models is still a challenge for many researchers due to its substantial computational costs. As such, there is growing interest in developing more affordable p…
ZEUS: Accelerating Diffusion Models with Only Second-Order Predictor
Yixiao Wang, Ting Jiang, Zishan Shao +6
Denoising generative models deliver high-fidelity generation but remain bottlenecked by inference latency due to the many iterative denoiser calls required during sampling. Trainin…
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…
KVCOMM: Online Cross-context KV-cache Communication for Efficient LLM-based Multi-agent Systems
Hancheng Ye, Zhengqi Gao, Mingyuan Ma +8
Multi-agent large language model (LLM) systems are increasingly adopted for complex language processing tasks that require communication and coordination among agents. However, the…