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Huaming Wu

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6 papers

1 author row

IJCAI Conference 2025 Conference Paper

FedCCH: Automatic Personalized Graph Federated Learning for Inter-Client and Intra-Client Heterogeneity

Pengfei Jiao
Zian Zhou
Meiting Xue
Huijun Tang
Zhidong Zhao
Huaming Wu

Graph federated learning (GFL) is increasingly utilized in domains such as social network analysis and recommendation systems, where non-IID data exist extensively and necessitate a strong emphasis on personalized learning. However, existing methods focus only on the personality among different clients instead of the personality within a client which widely exists in the real social networks, where intra-client personality addresses the heterogeneity of known data, while inter-client personality always tackle client heterogeneity under privacy constraint. In this paper, we propose a novel automatic personalized graph federated learning (PGFL) scheme named FedCCH to capture both inter-client and intra-client heterogeneity. For intra-client heterogeneity, we innovatively propose the learnable Personalized Factor (PF) to automatically normalize each graph representation within clients by learnable parameters, which weakens the impact of non-IID data distribution. For inter-client heterogeneity, we propose a novel hash-based similarity clustering method to generate the hash signature for each client, and then group similar clients for joint training among different clients. Ultimately, we collaboratively train intra-client and inter-client modules to improve the effectiveness of capturing the heterogeneity of the graph data of clients. Experiment results demonstrate that FedCCH outperforms other state-of-the-art baseline methods.

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TAAS Journal 2025 Journal Article

HAG-MTF: Higher-Order Adaptive Generative Graph for Massive Traffic Forecasting in Industry 5.0

Lei Wang
Huaming Wu
Fan Zhang
Keqiu Li
Wei Yu
Shuo Chen

With the evolution of urban smart transportation, the complexity of urban traffic networks escalates, emphasizing the importance of large-scale traffic data prediction in traffic management and urban planning. Traditional spatiotemporal graph models, such as Graph-WaveNet and MTGCN, face exponentially increasing computational complexity as the spatial dimensions expand. To address this challenge, we propose a novel Higher-order Adaptive Generative graph for Massive Traffic Forecasting (HAG-MTF) approach, which utilizes generative AI and high-order graph structures to model the intricate spatial dependencies in large-scale traffic data. The HAG-MTF incorporates a high-order dimensionality reduction module to optimize traffic node processing, utilizing prior graph relationships to generate a fusion graph that dynamically incorporates neighborhood information for efficient, localized graph convolution. The model further incorporates the high-order spatiotemporal relationship extraction module (H-net), enhancing the capacity and speed of traffic data processing while boosting prediction accuracy for complex spatial structures. Furthermore, HAG-MTF introduces a fusion loss function that hierarchically balances multiple objectives, ensuring both precision and computational efficiency. HAG-MTF adaptively handles large-scale real-world traffic data, meeting the needs of traffic controllers and urban planners for predicting massive datasets in practical settings. It supports efficient, flexible interactions via parameter tuning and model outputs, ultimately integrating human insights into traffic analysis and decision-making. This dynamic human-machine collaboration differs from non-Industry 5.0 approaches, which rely on purely automated systems without human input. Those lead to inflexible, brittle conclusions and recommendations, neglecting shifts in traffic patterns driven by human behavior. Extensive experiments on real-world traffic datasets demonstrate that HAG-MTF significantly improves processing efficiency for high-complexity spatial data while delivering precise, human-informed predictions through generative AI-driven operations.

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TAAS Journal 2025 Journal Article

Joint Optimization of Task Offloading Content Caching and Resource Allocation in Vehicular Edge Computing

Chaogang Tang
Huaming Wu
Ruidong Li
Joel J. P. C. Rodrigues

In Vehicular Edge Computing (VEC) environments, the increasingly complicated functional and non-functional requirements from vehicular applications such as MetaVehicles usually incur larger sizes of task-input data, which not only increase the transmission delay of task-input data via the front-haul links but also degrade the quality of experience for users, even if computation tasks can be offloaded and executed at the network edge. In this article, we put forward a caching-enabled task offloading strategy, by caching and reusing the universal context data at the edge server, to avoid duplicated data transmission in VEC systems. The goal is to minimize the overall response latency for all the tasks, by jointly optimizing task offloading, content caching, and resource allocation decisions in VEC. The optimization problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem. To efficiently solve this problem, we decompose this problem into two subproblems, namely, the computing Resource Allocation (RA) problem and the Joint Offloading and Caching (JOC) problem. The corresponding algorithms are put forward to solve the content caching and task offloading problems, respectively. Numeric evaluation reveals that our strategies and algorithms can achieve better performance in minimizing the overall response latency, in comparison with other approaches.

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JBHI Journal 2024 Journal Article

DRL-Based URLLC-Constraint and Energy-Efficient Task Offloading for Internet of Health Things

Yixiao Wang
Huaming Wu
Rutvij H. Jhaveri
Youcef Djenouri

Internet of Health Things (IoHT) is a promising e-Health paradigm that involves offloading numerous computational-intensive and delay-sensitive tasks from locally limited IoHT points to edge servers (ESs) with abundant computational resources in close proximity. However, existing computation offloading techniques struggle to meet the burgeoning health demands in ultra-reliable and low-latency communication (URLLC), one of the 5G application scenarios. This article proposes a Multi-Agent Soft-Actor-Critic-discrete based URLLC-constrained task offloading and resource allocation (MASACDUA) scheme to maximize throughput while minimizing power consumption on the remote side, considering the long-term URLLC constraints. The URLLC constraint conditions are formulated using extreme value theory, and Lyapunov optimization is employed to divide the problem into task offloading and computation resource allocation. MASAC-discrete and a queue backlog-aware algorithm are utilized to approach task offloading and computation resource allocation, respectively. Extensive simulation results demonstrate that MASACDUA outperforms traditional DRL algorithms under different IoHT points and data arrival rate intervals and achieves superior performance in delay, bound violation probability, and other characteristics related to URLLC.

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AAAI Conference 2024 Conference Paper

Enhancing Multi-Scale Diffusion Prediction via Sequential Hypergraphs and Adversarial Learning

Pengfei Jiao
Hongqian Chen
Qing Bao
Wang Zhang
Huaming Wu

Information diffusion prediction plays a crucial role in understanding the propagation of information in social networks, encompassing both macroscopic and microscopic prediction tasks. Macroscopic prediction estimates the overall impact of information diffusion, while microscopic prediction focuses on identifying the next user to be influenced. While prior research often concentrates on one of these aspects, a few tackle both concurrently. These two tasks provide complementary insights into the diffusion process at different levels, revealing common traits and unique attributes. The exploration of leveraging common features across these tasks to enhance information prediction remains an underexplored avenue. In this paper, we propose an intuitive and effective model that addresses both macroscopic and microscopic prediction tasks. Our approach considers the interactions and dynamics among cascades at the macro level and incorporates the social homophily of users in social networks at the micro level. Additionally, we introduce adversarial training and orthogonality constraints to ensure the integrity of shared features. Experimental results on four datasets demonstrate that our model significantly outperforms state-of-the-art methods.

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JBHI Journal 2024 Journal Article

Neural Networks Based Smart E-Health Application for the Prediction of Tuberculosis Using Serverless Computing

Subramaniam Subramanian Murugesan
Sasidharan Velu
Muhammed Golec
Huaming Wu
Sukhpal Singh Gill

The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advancements in the diagnosis and treatment of disease, which is reshaping the modern landscape of healthcare. In this paper, we propose a neural networks-based smart e-health application for the prediction of Tuberculosis (TB) using serverless computing. The performance of various Convolution Neural Network (CNN) architectures using transfer learning is evaluated to prove that this technique holds promise for enhancing the capabilities of IoT and e-health systems in the future for predicting the manifestation of TB in the lungs. The work involves training, validating, and comparing Densenet-201, VGG-19, and Mobilenet-V3-Small architectures based on performance metrics such as test binary accuracy, test loss, intersection over union, precision, recall, and F1 score. The findings hint at the potential of integrating these advanced Machine Learning (ML) models within IoT and e-health frameworks, thereby paving the way for more comprehensive and data-driven approaches to enable smart healthcare. The best-performing model, VGG-19, is selected for different deployment strategies using server and serless-based environments. We used JMeter to measure the performance of the deployed model, including the average response rate, throughput, and error rate. This study provides valuable insights into the selection and deployment of ML models in healthcare, highlighting the advantages and challenges of different deployment options. Furthermore, it also allows future studies to integrate such models into IoT and e-health systems, which could enhance healthcare outcomes through more informed and timely treatments.

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