...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Future generation computer systems >FedRD: Privacy-preserving adaptive Federated learning framework for intelligent hazardous Road Damage detection and warning
【24h】

FedRD: Privacy-preserving adaptive Federated learning framework for intelligent hazardous Road Damage detection and warning

机译：FEDRD：隐私保留适应性联邦学习框架，用于智能危险道路损伤检测和警告

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Road damages have caused numerous fatalities. Therefore, the study of road damage detection, especially hazardous road damage detection and warning, is critical in improving traffic safety. Existing road damage detection systems mainly process data on clouds, however, they are not able to warn users timely due to the long latency. Recent edge-computing techniques mitigate this problem while users can only receive warnings of hazardous road damages within a small area due to the limited communication range of edges. Besides, untrusted edges might misuse users' sensitive information. In this paper, we propose FedRD: a novel privacy-preserving edge-cloud and Federated learning-based framework for intelligent hazardous Road Damage detection and warning. In FedRD, a new hazardous road damage detection model is developed leveraging the advantages of hierarchical feature fusion. A novel adaptive federated learning strategy is designed for robust model learning from different edges with limited and unequally-sized datasets. A new individualized differential privacy approach with pixelization is proposed to protect users' privacy before sharing data. Simulation results demonstrate that FedRD achieves a high detection performance and provides fast responses with accurate warning information covering a wider area while preserving users' privacy, even when some edges have limited data.
机译：道路损害造成了许多死亡。因此，道路损伤检测，尤其是危险的道路损伤检测和警告的研究对于提高交通安全至关重要。现有的道路损伤检测系统主要处理云上的数据，但是，由于长期延迟，它们无法及时警告用户。最近的边缘计算技术减轻了这个问题，而由于边缘的通信范围有限，用户只能在一个小面积内接受危险道路损坏的警告。此外，不受信任的边缘可能会滥用用户的敏感信息。在本文中，我们提出了FEDRD：一种新的隐私保留边缘和联合学习的基于学习的智能危险道路损伤检测和警告的框架。在FEDRD中，开发了一种新的危险道路损伤检测模型，利用了等级特征融合的优点。一种新颖的自适应联合学习策略专为从具有有限和不均等大小的数据集的不同边缘的强大模型学习而设计。建议在共享数据之前保护具有像素化的新个性化差异隐私方法以保护用户的隐私。仿真结果表明，FEDRD在覆盖更广泛区域的准确警告信息的情况下，提供了快速响应，同时保留用户隐私，即使某些边缘具有有限的数据。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号