cs.AI updates on arXiv.org 07月23日 12:03
Decentralized AI-driven IoT Architecture for Privacy-Preserving and Latency-Optimized Healthcare in Pandemic and Critical Care Scenarios
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本文提出一种AI驱动的去中心化IoT架构,旨在解决传统集中式医疗架构的数据隐私、延迟和安全问题,通过增强现有联邦学习、区块链和边缘计算方法,实现数据隐私最大化、延迟最小化和系统性能提升,实验结果表明其性能优于云解决方案。

arXiv:2507.15859v1 Announce Type: cross Abstract: AI Innovations in the IoT for Real-Time Patient Monitoring On one hand, the current traditional centralized healthcare architecture poses numerous issues, including data privacy, delay, and security. Here, we present an AI-enabled decentralized IoT architecture that can address such challenges during a pandemic and critical care settings. This work presents our architecture to enhance the effectiveness of the current available federated learning, blockchain, and edge computing approach, maximizing data privacy, minimizing latency, and improving other general system metrics. Experimental results demonstrate transaction latency, energy consumption, and data throughput orders of magnitude lower than competitive cloud solutions.

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AI IoT 患者监控 联邦学习 区块链
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