Publications by Thanh Trung Huynh

Certified Unlearning for Federated Recommendation

Recommendation systems play a crucial role in providing web-based suggestion utilities by leveraging user behavior, preferences, and interests. In the context of privacy concerns and the proliferation of handheld devices, federated recommender systems have ...

ASSOC COMPUTING MACHINERY2025

On-device diagnostic recommendation with heterogeneous federated BlockNets

Thanh Trung Huynh

The evolution of edge computing has advanced the accessibility of E-health recommendation services, encompassing areas such as medical consultations, prescription guidance, and diagnostic assessments. Traditional methodologies predominantly utilize central ...

2025

Privacy-preserving explainable AI: a survey

Thanh Trung Huynh

As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanati ...

SCIENCE PRESS2025

High-Order Recommendation with Heterophilic Hypergraph Diffusion

Thanh Trung Huynh

Collaborative Filtering (CF) is a crucial task in recommendation systems, aimed at predicting user preferences based on the behaviors and preferences of similar users. While traditional CF techniques have achieved significant success by learning low-dimens ...

Springer Science and Business Media Deutschland GmbH2025

Fast Federated Unlearning in Skew Environments

Thanh Trung Huynh

Federated learning (FL) enables privacy-preserving model training but faces growing demands for unlearning-removing specific training data to enforce privacy rights and mitigate data poisoning. Existing unlearning methods, largely designed for centralized ...

2025

Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures

Thanh Trung Huynh

Recommender systems have become an integral part of online services due to their ability to help users locate specific information in a sea of data. However, existing studies show that some recommender systems are vulnerable to poisoning attacks, particula ...

2024

Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience

Karl Aberer, Thanh Trung Huynh

Federated learning (FL) has recently emerged as a compelling machine learning paradigm, prioritizing the protection of privacy for training data. The increasing demand to address issues such as "the right to be forgotten" and combat data poisoning attacks ...

Springer Nature2024

Self-supervised air quality estimation with graph neural network assistance and attention enhancement

Thanh Trung Huynh

The rapid progress of industrial development, urbanization, and traffic has caused air quality degradation that negatively affects human health and environmental sustainability, especially in developed countries. However, due to the limited number of senso ...

2024

Hierarchical Federated Learning in MEC Networks with Knowledge Distillation

Thanh Trung Huynh

Modern automobiles are equipped with advanced computing capabilities, allowing them to become powerful computing units capable of processing a large amount of data and training machine learning models. However, machine learning algorithms typically require ...

Institute of Electrical and Electronics Engineers Inc.2024

CARER - ClinicAl Reasoning-Enhanced Representation for Temporal Health Risk Prediction

Thanh Trung Huynh

The increasing availability of multimodal data from electronic health records (EHR) has paved the way for deep learning methods to improve diagnosis accuracy. However, deep learning models are data-driven, requiring large-scale datasets to achieve high gen ...

Association for Computational Linguistics (ACL)2024

A dual benchmarking study of facial forgery and facial forensics

Thanh Trung Huynh

In recent years, visual facial forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security. A wide range of malicious applications have emerged, such as deepfake, fake news, defa ...

2024

Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention

Thanh Trung Huynh

Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is to incorporate rich semantic information for more accurate recommendations. ...

2024

Modelling and solving a real-world truck-trailer scheduling problem in container transportation with separate moving objects

Thanh Trung Huynh, Van Son Nguyen

Container transportation is pivotal in global supply chains, facilitating the exchange of goods between companies across different countries. Given the exceedingly high operational costs of transporting containers, optimizing itinerary schedules can yield ...

Springer India2024

A Contrastive Learning and Graph-based Approach for Missing Modalities in Multimodal Federated Learning

Thanh Trung Huynh

Federated Learning has emerged as a decentralized method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared global model while keeping their data stored locally. How ...

Institute of Electrical and Electronics Engineers Inc.2024

FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization

Thanh Trung Huynh

Federated Learning (FL) is a method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared global model while storing their data locally. However, a significant challeng ...

Institute of Electrical and Electronics Engineers Inc.2024

Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction

Karl Aberer, Thanh Trung Huynh

Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data. Unlike other multivariate time-series data, stock markets show two unique characteristics: (i) multi-order dynamics, as stock prices are a.ect ...

Assoc Computing Machinery2023

Network Alignment With Holistic Embeddings

Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Chi Thang Duong, Thanh Trung Huynh

Network alignment is the task of identifying topologically and semantically similar nodes across (two) different networks. It plays an important role in various applications ranging from social network analysis to bioinformatic network interactions. Howeve ...

IEEE COMPUTER SOC2023

Validating functional redundancy with mixed generative adversarial networks

Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Trung-Dung Hoang, Thanh Trung Huynh

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query proc ...

ELSEVIER2023

Complex Representation Learning with Graph Convolutional Networks for Knowledge Graph Alignment

Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Thanh Trung Huynh

The task of discovering equivalent entities in knowledge graphs (KGs), so-called KG entity alignment, has drawn much attention to overcome the incompleteness problem of KGs. The majority of existing techniques learns the pointwise representations of entiti ...

Wiley-Hindawi2023

Scalable maximal subgraph mining with backbone-preserving graph convolutions

Karl Aberer, Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Thanh Trung Huynh

Maximal subgraph mining is increasingly important in various domains, including bioinformatics, genomics, and chemistry, as it helps identify common characteristics among a set of graphs and enables their classification into different categories. Existing ...

ELSEVIER SCIENCE INC2023