As the computing paradigm shifts from cloudcomputing to end-edge-cloud computing, it also supports artificial intelligence evolving from a centralized manner to a distributed one. In this paper, we provide a comprehe...
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As the computing paradigm shifts from cloudcomputing to end-edge-cloud computing, it also supports artificial intelligence evolving from a centralized manner to a distributed one. In this paper, we provide a comprehensive survey on the distributed artificial intelligence (DAI) empowered by end-edge-cloud computing (EECC), where the heterogeneous capabilities of on-device computing, edgecomputing, and cloudcomputing are orchestrated to satisfy the diverse requirements raised by resource-intensive and distributed AI computation. Particularly, we first introduce several mainstream computing paradigms and the benefits of the EECC paradigm in supporting distributed AI, as well as the fundamental technologies for distributed AI. We then derive a holistic taxonomy for the state-of-the-art optimization technologies that are empowered by EECC to boost distributed training and inference, respectively. After that, we point out security and privacy threats in DAI-EECC architecture and review the benefits and shortcomings of each enabling defense technology in accordance with the threats. Finally, we present some promising applications enabled by DAI-EECC and highlight several research challenges and open issues toward immersive performance acquisition.
The parallel distributed detection is studied for mobile wireless sensor networks (MWSNs) in the presence of Byzantine attacks in entirely unknown environment or complicated environment from the perspective of the inf...
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The parallel distributed detection is studied for mobile wireless sensor networks (MWSNs) in the presence of Byzantine attacks in entirely unknown environment or complicated environment from the perspective of the information theory, where we pay most of our attention toward design, analysis, and evaluation of the attack strategy. In particular, the multihop relay network and the harsh wireless communication condition, e.g., the dynamic and entirely unknown channel, are taken into consideration in our configuration. Second, the conditions that the optimal attacking strategy should satisfy is analyzed and developed under different attacking scenarios. Third, the minimum attacking power is developed for the Byzantines to blind the fusion center (FC). Furthermore, the optimal attacking strategies are developed when no prior information of the system is known for the Byzantines. Finally, the traditional four typical attack strategies are evaluated, and we find that the fraction of Byzantines is the only factor that affects the reliable data fusion when the network size and the attacking strategy are fixed. The extensive simulation is conducted to verify our design, analysis, and evaluation of the attack strategy.
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 int...
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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.
The Internet of Things (IoT) has attracted increasing interest from academia and industry. It facilitates many applications, such as the wireless voting system. Yet, current wireless voting terminal devices are mostly...
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As a three-terminal collaborative architecture of terminal, edge and cloud, end-edge-cloud is jointly participated by multiple institutions or organizations, and has the characteristics of a hybrid trust architecture ...
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