Q
Qinghua Lu
Researcher at Commonwealth Scientific and Industrial Research Organisation
Publications - 195
Citations - 3221
Qinghua Lu is an academic researcher from Commonwealth Scientific and Industrial Research Organisation. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 22, co-authored 140 publications receiving 1765 citations. Previous affiliations of Qinghua Lu include NICTA & University of New South Wales.
Papers
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Journal ArticleDOI
Adaptable Blockchain-Based Systems: A Case Study for Product Traceability
Qinghua Lu,Xiwei Xu +1 more
TL;DR: The OriginChain project as mentioned in this paper is a real-world traceability system using a blockchain, which provides transparent tamper-proof traceability information, automates regulatory compliance checking, and enables system adaptability.
Journal ArticleDOI
Blockchain-Based Federated Learning for Device Failure Detection in Industrial IoT
Weishan Zhang,Qinghua Lu,Qiuyu Yu,Zhaotong Li,Yue Liu,Sin Kit Lo,Shiping Chen,Xiwei Xu,Liming Zhu +8 more
TL;DR: To ensure client data privacy, a blockchain-based federated learning approach for device failure detection in IIoT is proposed, and a novel centroid distance weighted federated averaging algorithm taking into account the distance between positive class and negative class of each client data set is proposed.
Journal ArticleDOI
Designing blockchain-based applications a case study for imported product traceability
Xiwei Xu,Xiwei Xu,Qinghua Lu,Qinghua Lu,Yue Liu,Liming Zhu,Liming Zhu,Haonan Yao,Athanasios V. Vasilakos +8 more
TL;DR: This research presents a probabilistic architecture for solving the challenge of integrating NoSQL data stores and identity management systems to manage transactions across distributed systems.
Proceedings ArticleDOI
Evaluating Suitability of Applying Blockchain
TL;DR: This paper proposes an evaluation framework that comprises a list of criteria and a typical process for practitioners to assess the suitability of applying blockchain using these criteria based on the characteristics of the use cases.
Journal ArticleDOI
Dynamic-Fusion-Based Federated Learning for COVID-19 Detection
Weishan Zhang,Tao Zhou,Qinghua Lu,Xiao Wang,Chunsheng Zhu,Haoyun Sun,Zhipeng Wang,Sin Kit Lo,Fei-Yue Wang +8 more
TL;DR: The proposed novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections is feasible and performs better than the default setting of federatedLearning in terms of model performance, communication efficiency, and fault tolerance.