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相关概念视频

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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DistMLLM: Enhancing Multimodal Large Language Model Serving in Heterogeneous Edge Computing.

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相关实验视频

TCS-FEEL:拓优化的联合边缘学习与客户端选择.

Hui Chen1, He Li1

  • 1Department of Sciences and Informatics, Muroran Institute of Technology, Muroran 050-0071, Hokkaido, Japan.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
概括
此摘要是机器生成的。

通过使用拓意识的客户端选择,TCS-FEEL优化了联合学习 (FL). 这一框架改善了动态网络中的保护隐私,资源效率的边缘智能.

关键词:
边缘计算是一种边缘计算.联合学习的联合学习随机客户端选择选择 随机客户端选择拓优化优化拓学的优化无线网络是无线网络.

相关实验视频

科学领域:

  • 边缘计算 边缘计算
  • 机器学习 机器学习
  • 无线网络 无线网络 无线网络

背景情况:

  • 联合学习 (FL) 允许在边缘设备上进行保护隐私的分布式培训.
  • 无线网络中的统计和系统异质性阻碍了FL的性能.
  • 现有的FL方法与动态环境和高效的资源使用作斗争.

研究的目的:

  • 提出TCS-FEEL,一个拓意识的客户端选择框架,用于联合学习.
  • 解决边缘环境中统计异质性和系统动态的挑战.
  • 通过优化客户选择和沟通,增强保护隐私和资源效率的FL.

主要方法:

  • 开发了TCS-FEEL,集成了用户分布,设备对设备 (D2D) 通信和数据统计相似性,用于客户选择.
  • 实现了基于树的自适应性通信结构,随机采样客户端.
  • 利用边缘设备作为继电器来利用D2D传输以实现高效的模型聚合.

主要成果:

  • TCS-FEEL显著减少了训练轮的数量和每轮的壁表时间.
  • 该框架在各种非IID数据分布和移动设置中保持了高模型准确性.
  • 与现有基线相比,在MNIST和CIFAR-10的广泛实验中表现出优异的性能.

结论:

  • 将拓控制与客户端选择集成在一起,可以有效地加速联合学习.
  • 在富有传感器的动态边缘环境中,TCS-FEEL为保护隐私和资源高效的FL提供了强大的解决方案.
  • 这种方法非常适合用于自动驾驶,智能城市监控和工业物联网等应用.