D Zengxiang Li

SingHealth Duke-NUS Artificial Intelligence in Medicine Institute (AIMI)

Research and Projects

Principal Investigator, SingHealth Duke-NUS AIMI (06/2025-Present)
At the Artificial Intelligence in Medicine Institute (AIMI), Dr. Li leads a multidisciplinary R&D team on trustworthy and scalable AI for healthcare, foundation models, federated learning, and AI-agent systems across imaging, text, and time-series data. His work focuses on developing generalizable multimodal models, enabling privacy-preserving cross-institution collaboration, and building interpretable AI for clinical decision support. He drives end-to-end translation from research to deployment through national-scale programmes (e.g., SIMFONI, PULSES) and projects in physiological signals and federated AI, supporting robust, fair AI solutions, as well as data-driven healthcare optimization policymaking.

Principal Investigator, Singapore Eye Research Institute (SERI) (08/2025-Present)
Dr. Li leads federated learning and multimodal foundation model research on ophthalmology imaging AI, spanning diagnosis, prognosis, and oculomics, in close collaboration with SERI, national eye centres, and international partners.

Adjunct Associate Professor, Information Systems Technology and Design (ISTD), SUTD (06/2026-Present)
Dr. Li advances interdisciplinary research and education at the intersection of artificial intelligence and healthcare. He provides strategic leadership in AI research through the SingHealth–SUTD Collaboration, fostering multidisciplinary partnerships across healthcare, academia, and industry, supporting national research initiatives, and accelerating the translation of trustworthy AI technologies into clinical practice and population health. His work also contributes to strengthening Singapore's healthcare AI research ecosystem through collaborative research, talent development, and innovation.

Selected Projects

Singapore Ministry of Health and NMRC "Singapore Medical Foundation AI Model (SIMFONI) Programme", Role: Work Package Technical Lead (07/2025–Present)
Led the ophthalmology application of foundation models under a national AI programme, driving multimodal vision and vision-language model (VLM) research and deployment. Responsible for data curation, model design, and multi-center validation, enabling integration into clinical systems and workflows. Addressed cross-device heterogeneity and safe deployment challenges, supporting large-scale clinical decision-making and accelerating AI translation in Singapore's healthcare system.
Key partners: MOHT, A*STAR, AI.SG, SENC, Polyclinics
Singapore Ministry of Health, "Population-based, Unified, Learning System for Enhanced and Sustainable Health (PULSES Phase 3)", Role: Co-Lead of Applied AI & Data Science (09/2025–Present)
Co-led the development of large-scale data processing and analytics pipelines, incorporating automation and AI-agent-based systems to support population health research. Trained and deployed AI, deep learning, and foundation models on diverse public health and care datasets, while establishing AI evaluation and governance frameworks. Delivered data-driven insights to support national policy-making and integrated care system optimization, contributing to multiple projects across metabolic health, sleep, lifestyle, cognitive decline, and mental health for diverse population subgroups.
Key partners: NNI, Local hospitals and Communities
Multimodal Physiological Signal Foundation Models for Clinical AI (ECG, EEG, Wearables), Role: Initiator & Research Lead (2025–Present)
Initiated and led a collaborative research programme on foundation models for physiological signals, focusing on ECG, EEG, and continuous monitoring PPG/CGM. Defined core research directions including unified time-series foundation model, representation learning, multimodal data fusion, agentic report generation, and longitudinal EHR/world models. Coordinated cross-institution collaborations and supervised PhD projects. Established clinically meaningful problem statements and real-world pilot studies and validation.
Key partners: NTU, Duke-NUS, Griffith University, Emory University, SUTD
Federated Learning & Trustworthy AI for Healthcare, Role: Research Lead / Co-PI (2025–Present)
Developed novel federated learning approaches, including one-shot FL with prototype-guided Mixture-of-Experts (Fair-FedMOE), to address data heterogeneity, subgroup fairness, and model inconsistency. Designed and implemented cross-institution and cross-country FL frameworks supporting applications in post-transplant risk prediction, EEG-based epilepsy and seizure detection, and ophthalmology imaging AI (diagnosis, prognosis, and oculomics). Established end-to-end pipelines for secure data harmonization and efficient aggregation of diverse AI models, ensuring robustness, fairness, and regulatory compliance.
Key partners: SERI, NNI, CGMH, NTU, China University of Petroleum (East China)

Joint Appointments & Professional Service

  • Member, "LEAP" (Learning & Experimenting with AI Partnership) for Education Workgroup, Academic Medicine Education Institute (AMEI), SingHealth Duke-NUS Academic Medical Centre
  • Member, Artificial Intelligence Research Grant Review Committee (AIGRC), SingHealth Duke-NUS Academic Medical Centre
  • Steering Committee Member & Co-Lead of AI Research Domain, SingHealth – Singapore University of Technology and Design (SUTD) Collaborative Research Platform
  • Co-Lead, Applied AI & Data Science Core Technology, "Population-based, Unified, Learning System for Enhanced and Sustainable Health (PULSES)", Phase III
  • Contributor, Personal Data Protection Commission Singapore "Federated Learning Technical Guide" led by Info-Communications Media Development Authority, Singapore

Selected Journal Publications

Multimodal, Multi-Disease Medical Imaging Foundation Model (MerMED-FM)
Yang Zhou, Chrystie Wan Ning Quek, Jun Zhou, Yan Wang, Yang Bai, Zengxiang Li, Rick Siow Mong Goh, Yong Liu, Tien Yin Wong, Nan Liu, Daniel Shu Wei Ting, et al.
The Lancet Digital Health, IF 23.8, 2026 (Accepted)

EVLF-FM: Explainable Vision Language Foundation Model for Medicine
Yang Bai, Haoran Cheng, Yang Zhou, Jun Zhou, Arun Thirunavukarasu, Zengxiang Li, Tien Yin Wong, Nan Liu, Rick Siow Mong Goh, Yong Liu, Daniel Shu Wei Ting, et al.
Nature Medicine (Under Review)

Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
Yumeng Shao, Jun Li, Long Shi, Kang Wei, Ming Ding, Zengxiang Li, Wen Chen, Shi Jin, et al.
IEEE Transactions on Emerging Topics in Computing, 2026

Shadowed Set-based Three-way Clustering with Bilevel Aggregation for Personalized Federated Learning
Shubao Zhao, Sin G. Teo, Zengxiang Li
Future Generation Computer Systems (FGCS), IF 6.1, 2026 (Accepted)

Selected Conference Publications

Predicting Systemic Cardiometabolic Biomarkers Using Color Fundus Photography in the AI-READI Cohort
Laura Gutierrez, Haoran Cheng, Shawn Li Zengxiang, Kanae Fukutsu, Yao Jie, Chrystie Wan Ning Quek, Daniel Shu Wei Ting, et al.
Association for Research in Vision and Ophthalmology (ARVO) Annual Meeting, 2026

Retinal Fundus Image-Based Deep Learning for Detection of Obstructive Coronary Artery Disease
Jie Yao, Zengxiang Li, Kanae Fukutsu, Haoran Cheng, Daniel Shu Wei Ting, et al.
Association for Research in Vision and Ophthalmology (ARVO) Annual Meeting, 2026

AI Oculomics as a Biomarker to Predict Long Term Stroke Functional Outcome
Kanae Fukutsu, Yao Jie, Li Zengxiang, Woon Fung Peng, Deidre Anne De Silva, Daniel Ting, et al.
The 41st Asia-Pacific Academy of Ophthalmology Congress in conjunction with the 37th Annual Scientific Meeting, Hong Kong Ophthalmological Symposium, 2026

Horizon-Aware Vision–Language Forecasting of Diabetic Retinopathy with Text Prototypes
Gongyu Zhang, YT, Zengxiang Li, Timothy Jackson, Christos Bergeles, et al.
Workshop on Multi-Modal Foundation Models and Large Language Models for Life Sciences, in conjunction with NeurIPS 2025

Learning Cardiac Latent Representations in Vectorcardiogram Space
Bosong Huang, Panzhen Zhao, Zengxiang Li, Patricia Lee, Wei Jin, et al.
ICML 2026

See First, Reason Later: Mutual Information-Guided Reinforcement Learning for Vision-Language Models
Junfeng Fang, Zonghan Wu, Yin Zhang, Jiaxuan Zhao, Zengxiang Li, Kun Wang, et al.
ICML 2026

Fair-FedMOE: Group-Fair One-Shot Federated Learning via Prototype-Guided Experts for Medical Imaging Analysis
Lingzhao Meng, Shuai Guo, Weishan Zhang, Zengxiang Li, Han Yu, Nan Liu, Daniel Shu Wei Ting, et al.
ICML 2026

ENNEW Digital Technology Research Institute

Research and Projects

ENNEW Digital Technology Research Institute is committed to the research and development on industrial digitalization and artificial intelligence technology innovation, for smart energy, supply chain, smart city, healthy lifestyle and etc. In recent years, the Institute has focused on collaborative learning and foundation model for industrial applications, with fruitful R&D results, more than 480 invention patents applied, dozens of impactful papers published with top conference awards, and several technical standards released.

As a deputy director, I am leading a team of 30 researchers and engineers. We conduct impactful research on Industrial Internet, Artificial Intelligence, Time-series and Multi-Modal Foundation Model, Large Language Model (LLM) and AI-Agent, Collaborative Learning and Privacy-Preserving computing technologies, with a series of successful deployment for energy load forecasting, equipment predictive maintenance, heating system optimization, safety inspection and management, and etc. By Leveraging ENN Group’s industry leading position and advantages on various applications and data, we establish collaborations amongst government agencies, industry companies and universities for fundamental research, interoperable Federated Learning platform testbed and ecosystem for various Industrial Internet applications with a number of participants from different backgrounds and regions.

From 08/2023 to 08/2024, Dr. Li concurrently served as a Visiting Faculty member at Nanyang Technological University (NTU), Singapore, complementing his industry leadership role with academic teaching and research collaboration.

Research on Industrial AI Applications
Targeting the digital-intelligent transformation of ENN's core business, our focus lies on AI algorithms processing multimodal data. This includes prediction algorithms rooted in time-series data, anomaly detection and fault diagnosis based on IoT sensor data, computer vision models derived from cameras and specialized imaging tools (e.g., X-Ray and 3D-SLAM), and system optimization algorithms grounded in operations research, evolutionary algorithms, and reinforcement learning.
Research on Collaborative Learning and Industrial Application
Collaborative learning facilitates participants in co-creating superior AI models. Our platform accommodates a myriad of machine and deep learning collaborative algorithms and adaptive model aggregation strategies. Advanced AI technologies, including knowledge distillation, transfer learning, hard data sample selection, similarity clustering, adaptive model compression, are utilized to boost the efficiency and efficacy of collaborative learning amid heterogeneous data, models, and computing resources.
The platform also incorporates privacy-ensuring computing techniques to guarantee secure model aggregation and universal computation. Furthermore, participant contributions are measured fairly and efficiently, and thus well-designed incentive mechanism encourages contributing high-value data to cultivate a sustainable ecosystem.
Research on Multi-Modal Foundation Model
Drawing upon research in transformer-based hybrid DL networks, self-supervised learning, knowledge embedding and efficient fine-tuning methodologies, we pre-train time-series data, computer vision (CV), and vision-language multimodal foundation models using enterprise-wide data.
Our systematic experiments and real-world application validations suggest that these foundation models can acquire informative and meaningful data representations to support various downstream tasks. Their generalizability and adaptability to diverse application scenarios are crucial for widespread AI industry adoption.
Research on Large Language Model (LLM) and AI-Agent
With extensive project experience, I am well-versed in the full technology stack for fine-tuning large language models (LLMs), including high-quality data preparation and selection, cost-efficient fine-tuning strategies, and application-driven preference alignment. My work explores graph and structured data to enhance LLM capabilities in multi-hop reasoning, complex task planning and ensuring factual correctness.
Concurrently, I have led research into the alignment of time series data with LLMs and adaptive Retrieval-Augmented Generation (RAG) mechanisms for the efficient incorporation of domain knowledge. Building on this foundation, I have created a data and cognitive analytics AI-agent framework to support flexible and interactive business analytics, addressing needs such as natural gas demand and supply and optimizing industrial control systems like HVAC.
INDUSTRIAL APPLICATION: Natural gas supply chain forecasting and optimization
Collaborative learning with incentive mechanism enables natural gas companies sharing massive consumers’ data and leveraging consumers’ confidential working plan in a legal compliant manner, for the purpose of creating highly accurate load forecasting AI models.
Based on a vast amount of gas usage data and relevant influencing factors from residential and commercial users, train AI/foundation models for long-term and short-term forecasting, anomaly detection, imputation, classification, and other downstream AI tasks. Explore the power of Large Language Model to capture industry domain knowledge, interpret patterns and trends in time-series data, support cognitive reasoning and decision-making.
The research outcomes have been applied in a number of city gas retailers and heating stations for demand management, supply scheduling, and energy saving.
INDUSTRIAL APPLICATION: Energy and urban safety intelligent operation management
Utilize federated learning techniques to securely use data from energy companies, heating companies, property management companies, government departments, and other stakeholders. Jointly train and fine-tune vision-language multimodal foundation models for intelligent tasks such as open-world object detection and segmentation, image captioning and question answering.
The AI models have been deployed many application scenarios to identify safety hazards in gas pipelines and stations, residential indoor and public areas, streets and commercial premises, highlighting potential risks, and providing corresponding solutions based on collaboratively learned domain knowledge.
INDUSTRIAL APPLICATION: Equipment Predictive Maintenance
Based on equipment maintenance practices, develop integrated software and hardware solutions that combine IoT sensors and health management systems. By incorporating domain knowledge in vibration mechanisms, enhance the performance and interpretability of AI models to support anomaly detection, fault diagnosis, and lifespan prediction. Propose a cloud-edge collaborative learning framework to jointly-train foundation models efficiently amongst participations with heterogeneous data, computing power and local models.
The research results have been deployed in chemical plants, gas turbine factories, and energy stations, covering most rotating machinery in energy industry.

Selected Awards

  • Innovative Application of AI Award, AAAI 2023 and AAAI 2024 (Twice)
  • "ENN Collaborative Learning" obtained China Academy of Information and Communication Technology (CAICT) "Federated Learning" Certificate, 2022; "ENN Trusted Computing Framework" obtained CAICT "Trusted Execution Environment" Certificate, 2023
  • "AI Algorithm and Intelligent IoT-based LNG Unloading Supervision Innovation Application", Ministry of Industry and Information Technology (MIIT), China — National IoT Demonstration Project (Integrated Application Innovation Category), 2021
  • "Collaborative Learning Bi-directional Knowledge Distillation Technology", ENN Group Technology Invention Award, 2022
  • Best Application Award in FL-AAAI'2022, Innovation Award in FL-IJCAI'2022

Joint Appointments & Professional Service

  • Committee Member, NKU (Nankai University)-ENN Cognitive Technology Innovation Center
  • Founder Member, Trusted Artificial Intelligence Ecological Data Open Innovation Application Laboratory, Shandong Big Data Bureau
  • Vice President Unit, Alliance of Federated Learning Industrial Ecosystem Development (FLAL), led by Webank
  • Member, Privacy Preserving Computing Alliance, China Academy of Information and Communications Technology (CAICT)
  • Committee Member, Federated Data and Federated Intelligence Committee in Chinese Association of Automation
  • International Partner, Trusted Federated Learning Research Lab, Nanyang Technological University
  • IEEE Std 3652.1™-2020, "IEEE Guide for Architectural Framework and Application of Federated Machine Learning" Published
  • IEEE Std 2894™, "IEEE Guide for an Architectural Framework for Explainable Artificial Intelligence" Published
  • China Electronics Standardization Association, "Federated Learning for Industrial Scene Reference Architecture", Published

Selected Patents

  • Z. Yu, J. Yang, S. Liu, Z. Li, "Method and Apparatus for Predicting Flue Gas Oxygen Content in Gas Boilers," Patent No. CN202110039859.3, 13-Jan-2021
  • C. Yang, Z. Li, "Method and Apparatus for Determining Participant Contribution Values in Federated Learning," Patent No. CN202111326640.8, 10-Nov-2021
  • C. Yang, J. Liu, Z. Li, "Method, Apparatus, and Electronic Device for Determining Resource Contribution Based on Federated Learning," Patent No. CN202210129960.2, 11-Feb-2022
  • X. Wang, Z. Li, J. Liu, "Display Screen Panel for Simulation Analysis Visualization of Incentive Mechanism Graphical User Interface," Patent No. CN202330053673.3, 16-Feb-2023

Selected Journal Publications

Google Scholar | dblp (* = equal contributions)

Prompt Compression based on Key-Information Density
Yuhao Lin, Wenya Guo, Ying Zhang, Chengyi Yang, Zengxiang Li
Expert Systems with Applications, IF 7.5, Vol. 284, Jul 2025

Adversarial Federated Consensus Learning for Surface Defect Classification Under Data Heterogeneity in IIoT
Jixuan Cui, Jun Li, Zhen Mei, Yiyang Ni, Wen Chen, Zengxiang Li
IEEE Transactions on Instrumentation and Measurement, IF 5.9, Vol. 74, Aug 2025

Rethinking Masked Modeling for Time Series Forecasting: A Temporal Perspective
Shubao Zhao, Xinxing Zhou, Ming Jin, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Qingsong Wen, Yi Wang, Yanlong Wen, Xiaojie Yan
Knowledge-Based Systems, IF 8.8, Accepted

Smart Meter Data Sharing for AI-Enhanced Energy Systems
Ruiyang Yao, Jie Song, Zengxiang Li , Han Yu, Yi Wang
IEEE Power and Energy Magazine, Accepted

Heterogeneous Federated Learning Framework for IIoT Based on Selective Knowledge Distillation
Sheng Guo, Hui Chen, Yang Liu, Zengxiang Li
IEEE Transactions on Industrial Informatics, accepted

Advances and Open Challenges in Federated Learning with Foundation Models
Chao Ren, Han Yu, Hongyi Peng, Xiaoli Tang, Anran Li, Yulan Gao, Alysa Ziying Tan, Bo Zhao, Xiaoxiao Li, Zengxiang Li , Qiang Yang
IEEE Communications Surveys & Tutorials, IF 34, 2024

Shadowed Set-based Three-way Clustering with Bilevel Aggregation for Personalized Federated Learning
Shubao Zhao, Sin G. Teo, Zengxiang Li
Future Generation Computer Systems, 2024

Generative Image Reconstruction from Gradients
Ekanut Sotthiwat, Liangli Zhen, Chi Zhang, Zengxiang Li Rick Siow Mong Goh
IEEE Transactions on Neural Networks and Learning Systems, 2024

Clustering-based Contrastive Learning for Fault Diagnosis with Few Labelled Samples
Yajiao Dai, Zhen Mei, Jun Li, Zengxiang Li, Kang Wei, Ming Ding, Sheng Guo, Wen Chen,
IEEE Transactions on Instrumentation and Measurement, 2023

Industrial Big Data Analytical System in Industrial Cyber-Physical Systems Based on Coarse-to-Fine Deep Network
Ruonan Liu, Quanhu Zhang，Yu Wang, Yue Chen, Steven Ding, Qinghua Hu, Zengxiang Li, Yanbo Yang
IEEE Transactions on Industrial Informatics, 2023

Federated deep contrastive learning for mid-term natural gas demand forecasting
Dalin Qin, Guobing Liu, Zengxiang Li, Weicheng Guan, Shubao Zhao, Yi Wang
Applied Energy, 2023

Causal-Trivial Attention Graph Neural Network for Fault Diagnosis of Complex Industrial Processes
Hao Wang, Ruonan Liu, Steven Ding, Qinghua Hu, Zengxiang Li, Hongkuan Zhou
IEEE Transactions on Industrial Informatics, 2023

CFSL: A Credible Federated Self-Learning Framework
Weishan Zhang，Zhicheng Bao, Yuru Liu, Liang Xu, QinghuaLu, Huansheng Ning, Xiao Wang，Su Yang, Fei-Yue Wang, Zengxiang Li
IEEE Internet of Things Journal, 2023

WTDP-Shapley: Efficient and Effective Incentive Mechanism in Federated Learning for Intelligent Safety Inspection
Chengyi Yang, Jia Liu, Hao Sun, Tongzhi Li and Zengxiang Li
IEEE Transactions on Big Data TBD, IF 4.271, 2022

Augmented Multi-Party Computation for Secure Federated Learning
Chi Zhang, Sotthiwat Ekant, Liangli Zhen,Zengxiang Li
IEEE Transactions on Big Data TBD ,IF 4.271, 2022

CE-Fed: Communication Efficient Multi-party Computation Enabled Federated Learning
Renuga Kanagavelu,Qingsong Wei, Zengxiang Li ,Haibin Zhang,Juniarto Samsudin,Yechao Yang,Rick Siow Mong Goh,Shangguang Wang
ARRAY 2020 Open Access

R2Fed: Resilient Reinforcement Federated Learning for Industrial Applications
Weishan Zhang,Fa Yu, Xiao Wang,Xingjie Zeng, Hongwei Zhao, Zenglin Tian, Fei-Yue Wang, Hongwei Qi, Zengxiang Li
IEEE Transactions on Industrial Informatics, 2022

Selected Conference Publications

Multi-Session Budget Optimization for Forward Auction-based Federated Learning
Xiaoli Tang, Han Yu, Zengxiang Li, Xiaoxiao Li
Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM'25), 2025

A Bias-Free Revenue-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning
Xiaoli Tang, Han Yu, Zengxiang Li, Xiaoxiao Li
The 33rd International Joint Conference on Artificial Intelligence (IJCAI'24)

Himtm: Hierarchical multi-scale masked time series modeling for long-term forecasting
Shubao Zhao, Ming Jin, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Qingsong Wen,Yi Wang
The 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024) Full Research Paper Track

STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting
Zhengyang Zhao, Haitao Yuan, Nan Jiang, Minxiao Chen, Ning Liu, Zengxiang Li
International Conference on Database Systems for Advanced Applications (DASFAA'24)

Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models
Zengxiang Li, Zhaoxiang Hou, Hui Liu, Ying Wang, Tongzhi Li, Longfei Xie, Chao Shi, Chengyi Yang, Weishan Zhang, Zelei Liu,
Federated Foundation Models Workshop in Conjunction with the Web Conference 2024 (WebCon'24)

The Prospect of Enhancing Large-Scale Heterogeneous Federated Learning with Transformers
Yulan Gao, Zhaoxiang Hou，Chengyi Yang, Zengxiang Li, Han Yu,
IEEE International Conference on Multimedia and Expo (ICME 2024)

HiFi-Gas: Hierarchical Federated Learning Incentive Mechanism Enhanced Gas Usage Estimation
Hao Sun, Xiaoli Tang, Chengyi Yang, Zhenpeng Yu, Xiuli Wang, Qijie Ding, Zengxiang Li, Han Yu,
The 36th Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-24) AI deployment award

SWATM: Contribution-Aware Adaptive Federated Learning Framework Based On Augmented Shapley Values
Chengyi Yang, Zhaoxiang Hou, Sheng Guo, Hui Chen, Zengxiang Li
IEEE International Conference on Multimedia and Expo (ICME 2023)

Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout
Yuanyuan Chen, Zichen Chen, Yansong Zhao, Zelei Liu, Pengcheng Wu, Sheng Guo, Chengyi Yang, Zengxiang Li and Han Yu
The 35th Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-23) AI deployment award

WT-Shapley: Efficient and Effective Incentive Mechanism in Federated Learning for Intelligent Safety Inspection
Chengyi Yang, Jia Liu, Hao Sun, Tongzhi Li and Zengxiang Li
International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI 2022 Best Application Award

Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery
Sheng Guo, Zengxiang Li Hui Liu, Shubao Zhao and Cheng Hao Jin
International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI 2022

LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data
Lianlian Jiang, YuexuanWang, Wenyi Zheng, Chao Jin, Zengxiang Li, Sin G. Teo
International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI 2022

Cluster-driven Personalized Federated Learning for Natural Gas Load Forecasting
Shubao Zhao, Jia Liu, Guoliang Ma, Jie Yang, Di Liu and Zengxiang Li
FL-IJCAI 2022 : International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2022 (FL-IJCAI'22) Innovation Award

CluFL: Cluster-driven Weighted FL Model Aggregation Strategy
Hanchi Shen, Jun Li, Kang Wei, Pengcheng Xia, Sirui Tian, Ming Ding, Zengxiang Li
The 28th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2022)

Institute of High Performance Computing A*STAR

Research and Projects

Led a team of 15 researchers and engineers, and supervised several Ph.D and internship students. Played the role as PI and Co-PI for several impactful research programmes and industry projects, establishing close relations with universities, government agencies and industrial companies, to work together as an avid collaborator on urban computing and transportation, smart manufacturing, precision medicine, green data center, and etc. Published dozens of high-quality papers on ACM/IEEE Transactions, Journals, and Conferences, and also serves as track/workshop chair of several reputable international conferences.

Selected Awards

  • Grab AI for S.E.A Challenge "Traffic Management: Demand Prediction", Top 10 solution out of 1200 participants, Jul-2019
  • Amazon Research Grant "Blockchain and IoT Data Analytics for Fine-grained Transportation Insurance" (US$20000), Jun-2019
  • International Workshop on Blockchain Technologies and Systems (BCTS'18), 2018 Best Paper Runner Up
  • IEEE International Conference on Industrial Engineering and Engineering Management (IEEM'17), Honorable Mention Award, 2017
  • ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS'17), Best Paper Award, 2017
A*STAR SERC Strategic Fund: “Trusted Data Vault Phase 1” (Jan-2020 to Dec-2020)
Co-chair of the "Trusted Data Element Circulation" project to research and integrate cutting-edge technologies such as blockchain, secure multi-party computing, and federated learning to support cross-organizational secure data sharing and privacy protection for federated learning, and explore applications in healthcare, transportation, insurance, maritime, and manufacturing industries.
Key partners : I2R, ACRC, BMRC
Singapore-Germany Academic-Industry (2+2) International Collaboration Grant “SuppliedTrust: A Blockchain-based governance framework for transparent, efficient and trusted supply chain of unregulated consumer products”, Role: PI (Jan-2020 to Dec-2022)
Lead the project to develop a supply chain regulatory framework that integrates blockchain and privacy-preserving computing technologies to support transparent, efficient, and trustworthy cross-organizational supply chains for individual consumer goods.
Key partners: Kimberly-Clark, Fraunhofer Association, German Association for Technical Supervision.
Industry project “Transparent HPC”, Role: Key Member (Mar-2020 to Feb-2022)
Bigdata Analytics and Machine learning for adaptive resource provisioning for HPC applications from precision medicine DNA computing, fluid dynamic simulations.
Key partners:A*STAR ACRC, Fujitsu
Scalable Analytics Platform (ModStore) for National Precision Medicine Research (Oct-2019 to Oct-2021)
Develop a cloud-native data analytics platform to support large-scale genetic computing and medical data analytics.
Key partners:A*STAR GIS , SingHealth
Industrial Internet-of-Things Innovation (I3) Platform--Secure Platform for Trusted Collaborations work package (Oct-2018 to Oct-2021)
Responsible for the research and application of interoperability of various commercial industrial IoT platforms and the establishment of cross-organizational and upstream/downstream supply chain mutual trust cooperation mechanism based on blockchain technology.
Key partners:Rolls-Royce, AWS
Smart Manufacturing Joint Lab: “Knowledge-based Manufacturing (KBM) Industrial Internet of Things (IIOT) Shared Services (Feb-2018 to Dec-2020)
Responsible for the convergence of edge, local and cloud computing IoT platforms and solutions, machine learning algorithms and cloud-native microservices for manufacturing applications.
Key partner: Rolls-Royce
Urban Computing and Engineering Centre of Excellence in Singapore (CoE): “The Large-scale Data Processing Research (LDP) Research” (Jun-2015 to May-2018)
Responsible for high-speed parallel tools for large-scale city-level spatio-temporal data processing and analysis, as well as research on graph theory-based urban computing, exploring urban resource planning such as healthcare, multimodal transport connectivity, demand and arrival time forecasting, dial-a-ride and cab behavior analysis, school bus and ambulance scheduling, and other applications.
Key partners: Fujitsu and Land Transport Authority of Singapore.
Future Data Center Technology Research Program, Singapore Research Authority: Co-leader of the "Adaptive Integrated Resource Scheduling for Multi-User Data Centers" project (August 2012 to January 2015)
Responsible for adaptive scheduling of virtual computing resources to support efficient parallel distributed applications and energy efficient data centers.
Key partners:A*STAR DSI, NUS

Selected Journal Publications

Retinal Photograph-Based Deep Learning Algorithms for Myopia and a Blockchain Platform to Facilitate Artificial Intelligence Medical Research: A Retrospective Multi-Cohort Study
Tien-En Tan, Ayesha Anees, Cheng Chen, Shaohua Li, Xinxing Xu, Zengxiang Li , Tien Yin Wong, Yong Liu, Daniel Shu Wei Ting, et, al
The Lancet Digital Health, Mar, 2021 Editor’s Pick

A Survey of Smart Contract Formal Specification and Verification
Palina Tolmach, Yi Li, Shang-Wei Lin, Yang Liu, Zengxiang Li
ACM Computing Surveys, Mar, 2021

Augmented Multi-Party Computation for Secure Federated Learning
Chi Zhang Sotthiwat Ekanut; Liangli Zhen; Joey Tianyi Zhou; Zengxiang Li
IEEE Transactions on Big Data

Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusit Niyato, Zengxiang Li , Lingjuan Lyu, and Yingbo Liu
IEEE Internet of Things Journal, 2020

Blockchain and IoT for Insurance: A Case Study and Cyberinfrastructure Solution on Fine-grained Transportation Insurance
Zhe Xiao, Zengxiang Li , Yechao Yang, Yauheni Pyrloh, Ekanut Sotthiwat and Rick Siow Mong Goh
Transactions on Computational Social System, 2020

Reinforcement Learning enabled Genetic Algorithm for Vehicle Fleet Scheduling
Eda Koksal Ahmed, Zengxiang Li, Bharadwaj Veeravalli and Shen Ren
Journal of Intelligent Transportation Systems, 2020

Federated Learning for Advanced Manufacturing Based on Industrial IoT Data Analytics
Renuga Kanagavelu, Zengxiang Li, Juniarto Samsudin, Shaista Hussain, Yang Yechao, Yang Feng, Goh Siow Mong, Rick, Mervyn Cheah
Book chapter of “The Model Factory as the key enabler for the Future of Manufacturing”. Part of Book Series “Intelligent Systems Reference Library”, Springer-Verlag, 2020

An Effective Blockchain-based Decentralized Application for Smart Building System Management
Quanqing Xu, Zhaozheng He, Zengxiang Li, Mingzhong Xiao, Rick Siow Mong Goh, Yongjun Li
Book chapter of “Real-Time Data Analytics for Large-Scale Sensor Data", Elsevier 2019

Phase Transition in Taxi Dynamics and Impact of Ridesharing
Bo Yang, Shen Ren, Erika Legara, Zengxiang Li, Edward Ong, Louis Lin and Christophe Monterola
Transportation Science, 2019

Equality of Public Transit Connectivity: The Influence of MRT Services on Individual Buildings for Singapore
Zengxiang Li, Shen Ren, Nan Hu, Yong Liu, Zheng Qin, Rick Siow Mong Goh, Liwen Hou, Bharadwaj Veeravalli
Transportmetrica B: Transport Dynamics, 2018

Efficient Parallel Simulation over Large-scale Social Contact Networks
Yulin Wu, Wentong Cai, Zengxiang Li, Xiangting Hou, Wen Jun Tan
ACM Trans. on Modeling and Computer Simulation, 2018

Transparent three-phase Byzantine fault tolerance for parallel and distributed simulations
Zengxiang Li, Wentong Cai, Stephen John Turner, Zheng Qin, Rick Siow Mong Goh
Simulation Modelling Practice and Theory

Adaptive Resource Provisioning Mechanism in VEEs for Improving Performance of HLA-Based Simulations
Zengxiang Li, Wentong Cai, Stephen John Turner, Xiaorong Li, Ta Nguyen Binh Duong, Rick Siow Mong Goh
ACM Transactions on Modeling and Computer Simulation (TOMACS)

Un-identical federate replication structure for improving performance of HLA-based simulations
Zengxiang Li, Wentong Cai, Stephen John Turner
Simulation Modelling Practice and Theory(SMPT)

Selected Conference Publications

Partially Encrypted Multi-Party Computation for Federated Learning”, NEAC workshop IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing
Ekanut Sotthiwat, Liangli Zhen, Zengxiang Li, Chi Zhang
NEAC workshop IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021)
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Privacy-preserving Weighted Federated Learning within Oracle-Aided MPC Framework
Huafei Zhu, Zengxiang Li, Mervyn Cheah, Rick Siow Mong Goh,

Practical Secure Two-Party EdDSA Signature Generation with Key Protection and Applications in Cryptocurrency
Qi Feng, Debiao He, Zengxiang Li, Li Li, Kim-Kwang Raymond Choo,
IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2020)

An Analysis of Blockchain Consistency in Asynchronous Networks: Deriving a Neat Bound
Jun Zhao, Jing Tang, Zengxiang Li, Huaxiong Wang, Kwok-Yan Lam, Kaiping Xue
IEEE International Conference on Distributed Computing Systems (ICDCS 2020)

Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning
Renuga Kanagavelu, Zengxiang Li, Juniarto Samsudin, Yechao Yang, Feng Yang, Rick Siow Mong Goh, Mervyn Cheah, Praewpiraya Wiwatphonthana, Khajonpong Akkarajitsakul and Shangguang Wang
IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2020)

Efficient Multi-Party Computation Algorithm Design For Real-World Applications
Zengxiang Li, Chutima Kitcharoenpaisan, Phond Phunchongharnb, Yechao Yang, Rick Siow Mong Goh, and Yusen Li
International Workshop on Emerging Topic in Computer Science (ETCS 2019)

Blockchain and IoT Data Analytics for Fine-grained Transportation Insurance
Zengxiang Li, Zhe Xiao, Quanqing Xu, Ekanut Sotthiwat, Rick Siow Mong Goh and Xueping Liang
International Workshop on Blockchain Technologies and Systems (BCTS’18) 2018 Best Paper Runner Up

EMRShare: A Cross-organizational Medical Data Sharing and Management Framework Using Permissioned Blockchain
Zhe Xiao, Zengxiang Li, Yong Liu, Ling Feng, Weiwen Zhang, Thanarit Lertwuthikarn and Rick Siow Mong Goh
International Workshop on Blockchain Technologies and Systems (BCTS’18)

Building an Ethereum-based Decentralized Smart Home System
Quanqing Xu, Zhaozheng He, Zengxiang Li and Mingzhong Xiao
International Workshop on Blockchain Technologies and Systems (BCTS’18)

Traffic Speed Prediction with Convolutional Neural Network Adapted for Non-linear Spatio-temporal Dynamics
Shen Ren, Bo Yang, Liye Zhang and Zengxiang Li
ACM SIGSPATIAL International Workshop on analytics for Big Geospatial Data (BigSpatial 2018)

A Scalable Approach to Inferring Travel Time in Singapore’s Metro Network using Smart Card Data
Xi Lin, Xiaokui Xiao,Zengxiang Li
IEEE International Smart Cities Conference (ISC2 2018)

Path Travel Time Estimation using Attribute-related Hybrid Trajectories Network
Xi Lin, Yequan Wang, Xiaokui Xiao, Zengxiang Li and Sourav S. Bhowmick
ACM International Conference on Information and Knowledge Management (CIKM’19)

Cost-Efficient and Latency-Aware Workflow Scheduling Policy for Container-based Systems
Weiwen Zhang, Yong Liu, Long Wang, Zengxiang Li and Rick Siow Mong Goh,
IEEE International Conference on Parallel and Distributed Systems (ICPADS’18)

Concurrent Hybrid Breadth-First-Search on Distributed PowerGraph for Skewed Graphs
Zengxiang Li, Shen Ren, Sifei Lu, Jiachun Guo, Wentong Cai, Zheng Qin and Rick Siow Mong Goh
IEEE International Conference on Parallel and Distributed Systems (ICPADS’18) 2018

Optimize the FP-tree based Graph Edge Weight Computation on Multi-core MapReduce Clusters
Yuhong Feng, Meihong Guo, Kezhong Lu and Zhong Ming (Shenzhen University, China); Haoming Zhong (Webank, China); Wentong Cai (NTU, Singapore); Zengxiang Li (IHPC, Singapore)
IEEE International Conference on Parallel and Distributed Systems (ICPADS’17) 2017

Spatial-temporal Traffic Speed Bands Data Analysis and Prediction
Shen Ren, Lin Han, Zengxiang Li , Bharadwaj Veeravalli
IEEE International Conference on Industrial Engineering and Engineering Management (IEEM’17) 2017 Honorable Mention Award

A Hybrid Regression Technique for House Prices Prediction
Sifei Lu,Zengxiang Li , Zhen Qin, Xulei Yang, Rick Siow Mong GOH
IEEE International Conference on Industrial Engineering and Engineering Management (IEEM’17)

Performance Modelling and Cost Effective Execution for Distributed Graph Processing on Configurable VMs
Zengxiang Li , Bowen Zhang, Shen Ren, Yong Liu, Zheng Qin, Rick Siow Mong Goh, Mohan Gurusamy
International Symposium on Cluster, Cloud and Grid Computing (CCGrid’17)

Efficient Parallel Simulation over Social Contact Network with Skewed Degree Distribution
Yulin Wu, Xiangting Hou, Wen Jun Tan, Zengxiang Li, Wentong Cai
ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS’17) 2017 Best Paper Award

HubPPR: Effective Indexing for Approximate Personalized PageRank
Sibo Wang, Youze Tang, Xiaokui Xiao, Yin Yang, Zengxiang Li
International Conference on Very Large Data Bases (VLDB’17)

Ra2: Predicting simulation execution time for cloud-based design space explorations
Ta Nguyen Binh Duong, Jinghui Zhong, Wentong Cai,Zengxiang Li, Suiping Zhou
2016 IEEE/ACM 20th International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

Performance and monetary cost of large-scale distributed graph processing on amazon clouds
Zengxiang Li, Thai Nguyen Hung, Sifei Lu, Rick Siow Mong Goh
2016 International Conference on Cloud Computing Research and Innovations (ICCCRI)

Integrated QoS-aware resource provisioning for parallel and distributed applications
Zengxiang Li, Long Wang, Yu Zhang, Tram Truong-Huu, En Sheng Lim, Purnima Murali Mohan, Shibin Chen, Shuqin Ren, Mohan Gurusamy, Zheng Qin, Rick Siow Mong Goh
2015 IEEE/ACM 19th International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

HiPerData: An autonomous large-scale model building and management platform for big data analytics
Rubing Duan, Rick Siow Mong Goh, Feng Yang, Richard Di Shang, Yong Liu, Zengxiang Li, Long Wang, Sifei Lu, Xulei Yang, Zheng Qin
2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA)

Hierarchical parallelization and runtime scheduling for pregel-like graph processing systems
Zengxiang Li, Rubing Duan, Long Wang, Sifei Lu, Zheng Qin, Rick Siow Mong Goh
2014 IEEE 6th International Conference on Cloud Computing Technology and Science(CCTS)

Two-level storage QoS to manage performance for multiple tenants with multiple workloads
Shu Qin Ren, Shibin Cheng, Yu Zhang, En Sheng Lim, Khai Leong Yong, Zengxiang Li
2014 IEEE 6th International Conference on Cloud Computing Technology and Science(CCTS)

Hierarchical resource management for enhancing performance of large-scale simulations on data centers
Zengxiang Li, Xiaorong Li, Long Wang, Wentong Cai
Proceedings of the 2nd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation(PADS)

Selected Technology Disclosures

  • Carlos Toro, Zengxiang Li, Sifei Lu, Kanagavelu Renuga, “Evaluation Methodology for Industrial IoT (IIOT) Platforms In Manufacturing Industry”, TD-ARTC-2018-SMG-005, 22-May-2018
  • Zengxiang Li, Kanagavelu Renuga, Juniarto Samsudin, Feng Yang, Yechao Yang, Fei Gao, Rick Goh, Mervyn Cheah, “Federated Learning for Smart Manufacturing: Privacy-preserving collective machine learning on IoT data across multiple organizations”, IHPC-TD-CS-2019-024, Dec-2018
  • Zengxiang Li, Yang Yechao, Qingsong Wei, Zhu Huafei, Rick Goh, Mervyn Cheah, “A Practical Blockchain-based Trusted Data Sharing and Collaboration Platform Across Organizations”, IHPC-TD-CI-2020-001, Jan-2020

Education: Ph.D & Research Associate, Nanyang Technological University (NTU)

Dr. Li completed his Ph.D. at NTU's School of Computer Engineering (2006–2012), followed by a Research Associate appointment at the Parallel and Distributed Computing Centre, NTU (08/2010–06/2012), working on efficient and fault-tolerant HLA-based distributed simulation and its applications.

Ph.D Thesis

Efficient and Fault Tolerant HLA based Simulation

Abstract:The large scale distributed simulation executions may involve a large number of computationally intensive federates and thus are time and resource consuming. What is worse, these federates may be subject to crash-stop and Byzantine failures and the risk of federation failure increases with the federation scale. In this thesis, we propose mechanisms to support efficient and fault tolerant HLA-based simulation by exploiting the advantages of decoupled federate architecture, in which a federate connects to federation through its corresponding Decoupled RTI Component (DRC).
• Federate Migration Based on Decoupled Federate Architecture
• Federate Replication for Simulation Performance Enhancement
• Crash-Stop and Byzantine Fault Tolerance Mechanism Based on Checkpoint and Replication

Selected Journal Publications

Compensatory dead-reckoning-based update scheduling for distributed virtual environments
Zengxiang Li,Xueyan Tang, Wentong Cai, Xiaorong Li
Simulation

Loss-aware DR-based update scheduling for improving consistency in DVEs
Zengxiang Li,Wentong Cai, Xueyan Tang, Suiping Zhou
Journal of Simulation

A dynamic sort-based DDM matching algorithm for HLA applications
Ke Pan, Stephen John Turner, Wentong Cai,Zengxiang Li
ACM Transactions on Modeling and Computer Simulation

A replication structure for efficient and fault-tolerant parallel and distributed simulations
Zengxiang Li,Wentong Cai, Stephen John Turner, Ke Pan
Proceedings of the 2010 Spring Simulation Multiconference

A hybrid HLA time management algorithm based on both conditional and unconditional information
Ke Pan, Stephen John Turner, Wentong Cai, Zengxiang Li
Simulation

Selected Conference Publications

Accelerating optimistic HLA-based simulations in virtual execution environments
Zengxiang Li,Xiaorong Li, TA Nguyen Binh Duong, Wentong Cai, Stephen John Turner
Proceedings of the 1st ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

Fair and efficient dead reckoning-based update dissemination for distributed virtual environments
Zengxiang Li,Xueyan Tang, Wentong Cai, Stephen John Turner
2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation

Dead reckoning-based update scheduling against message loss for improving consistency in dves
Zengxiang Li,Wentong Cai, Xueyan Tang, Suiping Zhou
2011 IEEE Workshop on Principles of Advanced and Distributed Simulation

A Three-Phases Byzantine Fault Tolerance Mechanism for HLA-Based Simulation
Zengxiang Li,Wentong Cai, Stephen John Turner, Ke Pan
IEEE/ACM 14th International Symposium on Distributed Simulation and Real Time Applications (DS-RT'10)

Federate fault tolerance in HLA-based simulation
Zengxiang Li,Wentong Cai, Stephen John Turner, Ke Pan
2010 IEEE Workshop on Principles of Advanced and Distributed Simulation

Implementation of data distribution management services in a service oriented HLA RTI
Ke Pan, Stephen John Turner, Wentong Cai, Zengxiang Li
Proceedings of the 2009 Winter Simulation Conference

Multi-user gaming on the grid using a service oriented HLA RTI
Ke Pan, Stephen John Turner, Wentong Cai, Zengxiang Li
2009 13th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications

Improving performance by replicating simulations with alternative synchronization approaches
Zengxiang Li,Wentong Cai, Stephen John Turner, Ke Pan
2008 Winter Simulation Conference

A hybrid HLA time management algorithm based on both conditional and unconditional information
Ke Pan, Stephen John Turner, Wentong Cai, Zengxiang Li
2008 22nd Workshop on Principles of Advanced and Distributed Simulation

Federate migration in a service oriented hla rti
Zengxiang Li,Wentong Cai, Stephen John Turner, Ke Pan
11th IEEE International Symposium on Distributed Simulation and Real-Time Applications (DS-RT'07)

A service oriented HLA RTI on the grid
Ke Pan, Stephen John Turner, Wentong Cai, Zengxiang Li
IEEE International Conference on Web Services (ICWS 2007)

An efficient sort-based DDM matching algorithm for HLA applications with a large spatial environment
Ke Pan, Stephen John Turner, Wentong Cai, Zengxiang Li
21st International Workshop on Principles of Advanced and Distributed Simulation (PADS'07)