Edge-Cloud Collaborative Computation Offloading for Federated Learning in Smart City

Peng, Kai; Zhang, Haoqi; Zhao, Bohai; Liu, Peichen

doi:10.1109/dasc/picom/cbdcom/cy55231.2022.9927848

Cited by 2 publications

(1 citation statement)

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“…Another challenging issue of the urban environment refers to approaches aiming to reduce and manage network congestion in the Smart City, ensuring efficient resource utilization; the authors of [29] explore this direction through the exploitation of Service Function Chaining. The study presented in [30] explores the integration of federated learning (FL) technology in Smart Cities to handle network data while ensuring user privacy and security. It focuses on optimizing FL models' efficiency through computational offloading in edge-cloud collaborative environments.…”

Section: Related Workmentioning

confidence: 99%

Smart City as Cooperating Smart Areas: On the Way of Symbiotic Cyber–Physical Systems Environment

Tricomi,

Giacobbe,

Ficili

et al. 2024

Sensors

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The arising of the Cyber–Physical Systems’ vision and concepts drives technological evolution toward a new architectural design for the infrastructure of an environment referred to as a Smart Environment. This perspective alters the way systems within Smart City landscapes are conceived, designed, and ultimately realized. Modular architecture, resource-sharing techniques, and precise deployment approaches (such as microservices-oriented or reliant on the FaaS paradigm) serve as the cornerstones of a Smart City cognizant of multiple Cyber–Physical Systems composing it. This paper presents a framework integrating Digital Decisioning, encompassing the automated combination of human-derived knowledge and data-derived knowledge (e.g., business rules and machine learning), to enhance decision-making processes and application definition within the Smart City context.

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Section: Related Workmentioning

confidence: 99%