Cheng Huang

AIIM Journal 2026 Journal Article

Adaptive time-frequency decomposition informer for pathological rest tremor sequence prediction

• Feiyun Xiao
• Ruixue Gao
• Cheng Huang
• Jingsong Mu
• Yong Wang

JBHI Journal 2026 Journal Article

Integrating Differential Signals Into RNNs for Real-Time Respiratory Prediction

• Cheng Huang
• Yanping Lin

In lung nodule puncture and abdominal radiotherapy, the motion of lesions caused by patient respiration can compromise procedural accuracy. Consequently, integrating respiratory prediction can significantly enhance surgical precision. However, traditional methods often fail to provide real-time capabilities and may inadequately represent the dynamic characteristics of respiratory signals, resulting in potential inaccuracies and inefficiencies. In this study, we aimed to develop a novel real-time respiratory prediction model that incorporates respiratory signals along with their first- and second-order derivatives within a recurrent neural network framework. By incorporating differential signals, our model effectively learns the high-frequency characteristics of respiratory signals, enhancing its sensitivity to short-term respiratory fluctuations while preserving the ability to capture long-term dependencies. This approach addresses the limitations of current methods in handling nonlinear, quasi-periodic, and nonstationary respiratory signals. Our model achieved superior performance across prediction windows of 200, 400, and 600 ms, with mean absolute errors of 0. 026, 0. 216, and 0. 394 and root mean squared errors of 0. 034, 0. 288, and 0. 534, respectively. This study enhances the precision of respiratory prediction and delineates the critical role of differential signals in forecasting quasi-periodic physiological patterns.

EAAI Journal 2025 Journal Article

A novel high-accuracy graph neural network-based rumor detection method

• Xi Xiao
• Zeming Wu
• Chengzong Cai
• Tian Bian
• Guangwu Hu
• Qing Li
• Cheng Huang

AAAI Conference 2025 Conference Paper

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning

• Zhiqiang Li
• Haiyong Bao
• Menghong Guan
• Hao Pan
• Cheng Huang
• Hong-Ning Dai

Despite federated learning (FL)'s potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning (CFL) has emerged to address this challenge by partitioning users into clusters according to their similarity. However, CFL faces difficulties in training when users are unwilling to share their cluster identities due to privacy concerns. To address these issues, we present an innovative Efficient and Robust Secure Aggregation scheme for CFL, dubbed EBS-CFL. The proposed EBS-CFL supports effectively training CFL while maintaining users' cluster identity confidentially. Moreover, it detects potential poisonous attacks without compromising individual client gradients by discarding negatively correlated gradients and aggregating positively correlated ones using a weighted approach. The server also authenticates correct gradient encoding by clients. EBS-CFL has high efficiency with client-side overhead O(ml + m^2) for communication and O(m^2l) for computation, where m is the number of cluster identities, and l is the gradient size. When m = 1, EBS-CFL's computational efficiency of client is at least O(log(n)) times better than comparison schemes, where n is the number of clients. In addition, we validate the scheme through extensive experiments. Finally, we theoretically prove the scheme's security.

AAAI Conference 2025 Conference Paper

TC-Diffuser: Bi-Condition Multi-Modal Diffusion for Tropical Cyclone Forecasting

• Shiqi Zhang
• Pan Mu
• Cheng Huang
• Jinglin Zhang
• Cong Bai

Tropical cyclones (TCs) are complex weather systems with strong winds and heavy rainfall, causing substantial loss of life and property. Therefore, accurate TC forecasting is crucial for the effective prevention of disasters caused by TCs. TC forecasting can be regarded as a spatio-temporal prediction problem. It has been proven that using multi-modal data can effectively introduce atmospheric information to achieve better prediction results and higher interpretability. But it also introduces inevitably introduces noise into the prediction process. The diffusion model's unique noise modeling capability can reduce prediction noise when using multi-modal datasets. However, adapting it to TC forecasting has two main challenges: how to extract valuable information from multi-modal data, and how to utilize them to guide the generation process. For the first challenge, while recent methods can predict multiple TC attributes using multi-modal data, they often overlook the interdependence of multiple attributes and the semantic gap between modalities. Considering the interdependence of attributes, we propose two condition generators that capture the commonalities and characteristics of TC attributes, extracting spatio-temporal and environmental features and incorporating expert knowledge. To reduce the semantic gap between multi-modal data, we introduce the PGSA-LSTM module to map primary and auxiliary modalities. For the second challenge, we propose a novel Bi-condition diffusion model that sequentially processes conditions from the characteristics to commonalities of attributes, thereby expanding the guidance information that the diffusion model can accept. Our results surpass state-of-the-art deep learning models and outperform the numerical weather prediction model used by the China Central Meteorological Observatory. TC-Diffuser shows high generalizability across global ocean areas, strong robustness in handling missing data, and higher computational efficiency.

ICML Conference 2025 Conference Paper

TCP-Diffusion: A Multi-modal Diffusion Model for Global Tropical Cyclone Precipitation Forecasting with Change Awareness

• Cheng Huang
• Pan Mu
• Cong Bai
• Peter AG Watson

Deep learning methods have made significant progress in regular rainfall forecasting, yet the more hazardous tropical cyclone (TC) rainfall has not received the same attention. While regular rainfall models can offer valuable insights for designing TC rainfall forecasting models, most existing methods suffer from cumulative errors and lack physical consistency. Additionally, these methods overlook the importance of meteorological factors in TC rainfall and their integration with the numerical weather prediction (NWP) model. To address these issues, we propose Tropical Cyclone Precipitation Diffusion (TCP-Diffusion), a multi-modal model for forecasting of TC precipitation given an existing TC in any location globally. It forecasts rainfall around the TC center for the next 12 hours at 3 hourly resolution based on past rainfall observations and multi-modal environmental variables. Adjacent residual prediction (ARP) changes the training target from the absolute rainfall value to the rainfall trend and gives our model the capability of rainfall change awareness, reducing cumulative errors and ensuring physical consistency. Considering the influence of TC-related meteorological factors and the useful information from NWP model forecasts, we propose a multi-model framework with specialized encoders to extract richer information from environmental variables and results provided by NWP models. The results of extensive experiments show that our method outperforms other DL methods and the NWP method from the European Centre for Medium-Range Weather Forecasts (ECMWF).

EAAI Journal 2024 Journal Article

An enhanced algorithm for object detection based on generative adversarial structure

• Yun Zhang
• Cheng Huang
• Yuyao Zhang
• Shujuan Yu
• Liya Huang
• Na Xie

ECAI Conference 2024 Conference Paper

Phy-CoCo: Physical Constraint-Based Correlation Learning for Tropical Cyclone Intensity and Size Estimation

• Hanting Yan
• Pan Mu
• Cheng Huang
• Jinglin Zhang
• Cong Bai

Tropical Cyclone (TC) estimation aims to estimate various attributes of TC in real-time to alleviate and prevent disasters caused by violent TCs. As artificial intelligence technology advances, various deep learning-based multi-task estimation approaches have been proposed. However, most of them only focus on extracting common features of tasks, disregarding potential negative transfer and task interactions between different tasks. This paper is thus motivated to propose a Physical Constraint-based Correlation (Phy-CoCo) learning framework from the perspective of Multi-Task Learning (MTL). Specifically, for task-specific feature learning, we introduce Correlation Modeling (CoM) based on Centrally Expanded Pooling (CEP). Furthermore, for cross-task interaction, we propose a Multi-Domain Recurrent Convolution (MDRC) module to incorporate physical constraints into MTL. These physical constraints enable the transformation of different task features by simulating the physical relations among different attributes of TC. Lastly, in combination with a task-shared network that leverages the hybrid fusion of multi-modal data, our MTL framework accurately estimates various TC attributes. Extensive experiments conducted on our constructed dataset demonstrate that the proposed Phy-CoCo outperforms previous methods in TC estimation in terms of estimation error, verifying the potential of the physics-incorporated MTL model.

AAAI Conference 2023 Conference Paper

MGTCF: Multi-Generator Tropical Cyclone Forecasting with Heterogeneous Meteorological Data

• Cheng Huang
• Cong Bai
• Sixian Chan
• Jinglin Zhang
• YuQuan Wu

Accurate forecasting of tropical cyclone (TC) plays a critical role in the prevention and defense of TC disasters. We must explore a more accurate method for TC prediction. Deep learning methods are increasingly being implemented to make TC prediction more accurate. However, most existing methods lack a generic framework for adapting heterogeneous meteorological data and do not focus on the importance of the environment. Therefore, we propose a Multi-Generator Tropical Cyclone Forecasting model (MGTCF), a generic, extensible, multi-modal TC prediction model with the key modules of Generator Chooser Network (GC-Net) and Environment Net (Env-Net). The proposed method can utilize heterogeneous meteorologic data efficiently and mine environmental factors. In addition, the Multi-generator with Generator Chooser Net is proposed to tackle the drawbacks of single-generator TC prediction methods: the prediction of undesired out-of-distribution samples and the problems stemming from insufficient learning ability. To prove the effectiveness of MGTCF, we conduct extensive experiments on the China Meteorological Administration Tropical Cyclone Best Track Dataset. MGTCF obtains better performance compared with other deep learning methods and outperforms the official prediction method of the China Central Meteorological Observatory in most indexes.