Titlebook: Algorithms and Architectures for Parallel Processing; 23rd International C Zahir Tari,Keqiu Li,Hongyi Wu Conference proceedings 2024 The Ed

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Nomogram

,Joint Controller Placement and?Flow Assignment in?Software-Defined Edge Networks,lers are enabled to centralize network configuration and management. However, frequent flow fluctuations at the edge can result in a large backlog of local flow requests in the processing queue of the controllers, leading to high response delays. Although optimized controller placement and assignmen

漂白

,Distributed Latency-Efficient Beaconing for?Multi-channel Asynchronous Duty-Cycled IoT Networks,f Minimum Latency Beaconing Schedule (MLBS), which tries to obtain a fastest and collision-free schedule is not well studied when the IoT devices employ the duty-cycled working mode. The existing works have rigid assumptions that there exists a single channel in networks, and can only work in a cent

Frequency

抒情短詩(shī)

,SMCoEdge: Simultaneous Multi-server Offloading for?Collaborative Mobile Edge Computing,applications. However, current approaches typically perform offline task partitioning and offload each subtask to an Edge Server (ES) for processing. This leads to varying delays in subtask processing across different ESs, resulting in a high make-span of task offloading. To address this issue, we p

高原

,Image Inpainting Forensics Algorithm Based on?Dual-Domain Encoder-Decoder Network,inted images, which pose serious security issues. At present, there are relatively few forensic works on image inpainting. Moreover, there is a problem of poor generalization. Therefore, This paper proposes a dual-domain encoder-decoder network (DDEDNet) based on different input types, which is a tw

棲息地

incision

,FRAIM: A Feature Importance-Aware Incentive Mechanism for?Vertical Federated Learning,dering the selfishness of organizations, they will not participate in federated learning without compensation. To address this problem, in this paper, we design a feature importance-aware vertical federated learning incentive mechanism. We first synthesize a small amount of data locally using the in

LIMIT

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