Search Results for author: Gang Hu

Found 12 papers, 4 papers with code

Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization

no code implementations21 Nov 2024 Yunrui Sun, Gang Hu, Yinglei Teng, Dunbo Cai

Split Learning (SL) is a promising collaborative machine learning approach, enabling resource-constrained devices to train models without sharing raw data, while reducing computational load and preserving privacy simultaneously.

Federated Learning

Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approach

no code implementations14 Jun 2024 Gang Hu, Yinglei Teng, Nan Wang, Zhu Han

Federated Edge Learning (FEEL) emerges as a pioneering distributed machine learning paradigm for the 6G Hyper-Connectivity, harnessing data from the Internet of Things (IoT) devices while upholding data privacy.

Clustering Stochastic Optimization

Markowitz Meets Bellman: Knowledge-distilled Reinforcement Learning for Portfolio Management

no code implementations8 May 2024 Gang Hu, Ming Gu

This paper introduces a hybrid approach combining Markowitz's portfolio theory with reinforcement learning, utilizing knowledge distillation for training agents.

Knowledge Distillation Management +2

No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks

2 code implementations10 Mar 2024 Gang Hu, Ke Qin, Chenhan Yuan, Min Peng, Alejandro Lopez-Lira, Benyou Wang, Sophia Ananiadou, Jimin Huang, Qianqian Xie

While the progression of Large Language Models (LLMs) has notably propelled financial analysis, their application has largely been confined to singular language realms, leaving untapped the potential of bilingual Chinese-English capacity.

Financial Analysis

FinBen: A Holistic Financial Benchmark for Large Language Models

2 code implementations20 Feb 2024 Qianqian Xie, Weiguang Han, Zhengyu Chen, Ruoyu Xiang, Xiao Zhang, Yueru He, Mengxi Xiao, Dong Li, Yongfu Dai, Duanyu Feng, Yijing Xu, Haoqiang Kang, Ziyan Kuang, Chenhan Yuan, Kailai Yang, Zheheng Luo, Tianlin Zhang, Zhiwei Liu, Guojun Xiong, Zhiyang Deng, Yuechen Jiang, Zhiyuan Yao, Haohang Li, Yangyang Yu, Gang Hu, Jiajia Huang, Xiao-Yang Liu, Alejandro Lopez-Lira, Benyou Wang, Yanzhao Lai, Hao Wang, Min Peng, Sophia Ananiadou, Jimin Huang

Our evaluation of 15 representative LLMs, including GPT-4, ChatGPT, and the latest Gemini, reveals several key findings: While LLMs excel in IE and textual analysis, they struggle with advanced reasoning and complex tasks like text generation and forecasting.

Question Answering RAG +2

Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models

no code implementations9 Nov 2023 Gang Hu

This study enhances a Deep Q-Network (DQN) trading model by incorporating advanced techniques like Prioritized Experience Replay, Regularized Q-Learning, Noisy Networks, Dueling, and Double DQN.

Algorithmic Trading Q-Learning

Clustered Data Sharing for Non-IID Federated Learning over Wireless Networks

no code implementations17 Feb 2023 Gang Hu, Yinglei Teng, Nan Wang, F. Richard Yu

Federated Learning (FL) is a novel distributed machine learning approach to leverage data from Internet of Things (IoT) devices while maintaining data privacy.

Clustering Federated Learning +1

Investigation of wind pressures on tall building under interference effects using machine learning techniques

no code implementations20 Aug 2019 Gang Hu, Lingbo Liu, DaCheng Tao, Jie Song, K. C. S. Kwok

This study used machine learning techniques to resolve the conflicting requirement between limited wind tunnel tests that produce unreliable results and a completed investigation of the interference effects that is costly and time-consuming.

BIG-bench Machine Learning

Predicting wind pressures around circular cylinders using machine learning techniques

no code implementations21 Jan 2019 Gang Hu, K. C. S. Kwok

Numerous studies have been carried out to measure wind pressures around circular cylinders since the early 20th century due to its engineering significance.

BIG-bench Machine Learning

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