物联网联合区块链学习在边缘
概括
本研究介绍了一种用于医疗物联网 (IoMT) 设备的新型分布式联合学习框架. 它利用区块链提高隐私和效率,将计算从云端转移到边缘,以实现更好的医疗人工智能开发.
科学领域:
- 医疗信息学 医疗信息学
- 分布式系统 分布式系统
- 人工智能的人工智能
背景情况:
- 物联网 (IoT) 设备在医疗中未得到充分利用,尽管它们具有好处.
- 目前用于医疗机器学习的基于云的架构面临隐私和效率的挑战.
研究的目的:
- 为医疗物联网 (IoMT) 设备提出一个去中心化的联合学习框架.
- 加强隐私,效率和协作模式培训在医疗应用的边缘.
主要方法:
- 为IoMT设备开发了一个使用区块链的分布式联合学习框架.
- 实施了三个范式:在物联网设备上进行协作神经网络培训,私人IoMT系统培训和分布式网络培训分发.
主要成果:
- 实现协作模型培训,同时将学习与敏感数据集脱,确保隐私.
- 促进IoMT系统的私人培训,对于机密医疗数据至关重要.
- 允许医院利用备用计算资源进行分布式网络模型培训.
结论:
- 拟议的框架为IoMT.提供了一个分散的,保护隐私的,有效的替代方案,与集中式云架构为IoMT.
- 这种方法支持动态适应和对现实数据的培训,推进医学中的机器学习.
相关概念视频
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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