111 citations · 279 across the 7 of their papers we have counts for
8 papers
FLAMMABLE: A Multi-Model Federated Learning Framework with Multi-Model Engagement and Adaptive Batch Sizes
Shouxu Lin, Zimeng Pan, Yuhang Yao +3
Multi-Model Federated Learning (MMFL) is an emerging direction in Federated Learning (FL) where multiple models are trained in parallel, generally on various datasets. Optimizing t…
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3
Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…
LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction
Sungmin Lee, Minju Kang, Joonhee Lee +6
Question-answering (QA) interfaces powered by large language models (LLMs) present a promising direction for improving interactivity with HVAC system insights, particularly for non…
Qwen3 Technical Report
An Yang, Anfeng Li, Baosong Yang +57
In this work, we present Qwen3, the latest version of the Qwen model family. Qwen3 comprises a series of large language models (LLMs) designed to advance performance, efficiency, a…
Qwen2.5 Technical Report
Qwen, :, An Yang +41
In this report, we introduce Qwen2.5, a comprehensive series of large language models (LLMs) designed to meet diverse needs. Compared to previous iterations, Qwen 2.5 has been sign…
Electric Vehicle User Charging Behavior Analysis Integrating Psychological and Environmental Factors: A Statistical-Driven LLM based Agent Approach
Chuanlin Zhang, Junkang Feng, Chenggang Cui +6
With the growing adoption of electric vehicles (EVs), understanding user charging behavior has become critical for grid stability and transportation planning. This study investigat…
Qwen2 Technical Report
An Yang, Baosong Yang, Binyuan Hui +59
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models. We release a comprehensive suite of foundational and instruct…
Improving Local Training in Federated Learning via Temperature Scaling
Kichang Lee, Pei Zhang, Songkuk Kim +1
Federated learning is inherently hampered by data heterogeneity: non-i.i.d. training data over local clients. We propose a novel model training approach for federated learning, FLe…