Browsing by Author "Wang, Kevin I-Kai"

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2D Federated Learning for Personalized Human Activity Recognition in Cyber-Physical-Social Systems
(2022) Zhou, Xiaokang; Liang, Wei; Ma, Jianhua; Yan, Zheng; Wang, Kevin I-Kai
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
In this study, a 2-Dimensional Federated Learning (2DFL) framework, including the vertical and horizontal federated learning phases, is designed to cope with the insufficient training data and insecure data sharing issues in CPSS during a secure distributed learning process. Considering a specific application of Human Activity Recognition (HAR) across a variety of different devices from multiple individual users, the vertical federated learning scheme is developed to integrate shareable features from heterogeneous data across different devices into a full feature space, and the horizontal federated learning scheme is developed to effectively aggregate the encrypted local models among multiple individual users to achieve a high-quality global HAR model. A computationally efficient somewhat homomorphic encryption (SWHE) scheme is then improved and applied to support the parameter aggregation without giving access to it, which enables heterogeneous data sharing with privacy protection across different personal devices and multiple users in building a more precise personalized HAR model. Experiments are conducted based on two public datasets. Comparing with three conventional machine learning methods, evaluation results demonstrate the usefulness and effectiveness of our proposed model in achieving faster and smoother convergence, and better precision, recall, and F1 scores for HAR applications in CPSS.
A service-oriented programming approach for dynamic distributed manufacturing systems
(2020-01-01) Atmojo, Udayanto Dwi; Salcic, Zoran; Wang, Kevin I-Kai; Vyatkin, Valeriy
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Dynamic reconfigurability and adaptability are crucial features of the future manufacturing systems that must be supported by adequate software technologies. Currently, they are typically achieved as add-ons to existing software tools and run-time systems, which are not based on any formal foundation such as formal model of computation (MoC). This paper presents the new programming paradigm of service oriented SystemJ (SOSJ), which targets dynamic distributed software systems suited for future manufacturing applications. SOSJ is built on a merger and the synergies of two programming concepts of service oriented architecture, to support dynamic software system composition, and SystemJ programming language based on a formal MoC, which targets correct by construction design of static distributed software systems. The resulting programming paradigm allows the design and implementation of dynamic distributed software systems.
Two-layer Federated Learning with Heterogeneous Model Aggregation for 6G Supported Internet of Vehicles
(2021-06) Zhou, Xiaokang; Liang, Wei; She, Jinhua; Yan, Zheng; Wang, Kevin I-Kai
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The vision of the upcoming 6G technologies that have fast data rate, low latency, and ultra-dense network, draws great attentions to the Internet of Vehicles (IoV) and Vehicle-to-Everything (V2X) communication for intelligent transportation systems. There is an urgent need for distributed machine learning techniques that can take advantages of massive interconnected networks with explosive amount of heterogeneous data generated at the network edge. In this study, a two-layer federated learning model is proposed to take advantages of the distributed end-edge-cloud architecture typical in 6G environment, and to achieve a more efficient and more accurate learning while ensuring data privacy protection and reducing communication overheads. A novel multi-layer heterogeneous model selection and aggregation scheme is designed as a part of the federated learning process to better utilize the local and global contexts of individual vehicles and road side units (RSUs) in 6G supported vehicular networks. This context-aware distributed learning mechanism is then developed and applied to address intelligent object detection, which is one of the most critical challenges in modern intelligent transportation systems with autonomous vehicles. Evaluation results showed that the proposed method, which demonstrates a higher learning accuracy with better precision, recall and F1 score, outperforms other state-of-the-art methods under 6G network configuration by achieving faster convergence, and scales better with larger numbers of RSUs involved in the learning process.