Coarse-grained reconfigurable arrays (CGRAs) belong to the family of configurable processing architectures that have recently attracted increasing interest for their adaptability and efficiency. Research on CGRA archi...
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Through the fast growth of infrastructure development, the geotechnical engineering construction safety measure becomes the significant attention because of its complexity and the variation. Hastening the data of the ...
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The objective of this paper is to explore an effective approach for detecting anomalies in cryptocurrency transactions using artificial intelligence techniques. The proposed method is compared with existing approaches...
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In vehicular ad-hoc networks, vehicle-to-vehicle fog computing (VFC) can not only alleviate the computing delay of inference tasks from vehicles, but also reduce the computational overheads of RSUs. Existing studies o...
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Federated Learning (FL) emerged as a leading secure, distributed learning technology based on sharing insights instead of data. The privacy-ensuring capability of FL has enabled its extensive use in Data-Sensitive App...
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Federated Learning (FL) emerged as a leading secure, distributed learning technology based on sharing insights instead of data. The privacy-ensuring capability of FL has enabled its extensive use in Data-Sensitive Applications like healthcare and finance. However, the transmitted insights are at risk of leakage as the security of the medium cannot be guaranteed and can lead to the inference of the user data. Quantization is sometimes used to change these transmitted values to provide security but at the cost of accuracy loss in global models. Coupled with client dropouts, this increases performance loss. In this paper, we propose a Federated Workload-Aware Framework with Linear Quantization (Fed-WALQ), which layers the quantization process with an active client-selection technique based on the sustainable workload of the clients. The framework minimizes the dropout rates and compensates for the loss due to quantization. Through numerical experiments compared against traditional FL and Quantization-enabled FL over multiple datasets, the Fed-WALQ shows improvements in security over the former and accuracy over the latter. The accuracy improvement varies with the complexities of the involved datasets, while a substantial drop in straggler node percentages is seen in all cases (up to 91.8% drop).
To address the online learning problem in distributedsystems, Streaming Federated learning (SFL) enables immediate model training by clients upon collecting new data, finding wide applications in AI-enabled Internet-...
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Internet of Things (IoT) systems is widely used in many applications which generate huge amounts of sensory signals that involves to signify the state of systems. However, due to changes in IoT environment impact the ...
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Edge Computing (EC) extends computing functionalities from remote cloud data centers to the proximity of end-user devices at the network edges, thereby reducing application response time. However, existing solutions f...
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Edge Computing (EC) extends computing functionalities from remote cloud data centers to the proximity of end-user devices at the network edges, thereby reducing application response time. However, existing solutions face challenges in scalability, efficient resource management, and near real-time adaptation in dynamic environments. In this paper, we jointly investigate the application placement and load distribution challenges in an EC-enabled mobile network. We propose a hierarchical distributed Limited Look-Ahead Control (LLC) approach that mitigates centralized control bottlenecks by breaking down the overall decision problem into a series of local problems solved cooperatively through a two-tier architecture. The global controller processes system-wide information and sets local constraints, while local controllers make autonomous decisions coordinated through mutual information exchange. Utilizing LLC allows for anticipation and adaptation to load fluctuations. Even though the results indicate that the trade-off between system performance and scalable decisions depends on how the overall problem is decomposed, our distributed solution significantly reduces the controller's execution time compared to a centralized approach.
This study investigates the robustness of a Software-defined Networking (SDN) controller when confronted with a distributed Denial-of-Service (DDOS) attack in a tactical environment. A proactive defense mechanism is i...
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The implementation of a spiking neural network (SNN) with memristor arrays has the potential to improve the area and power efficiency of edge-device inference. However, due to non-linearity, we cannot maintain the inf...
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