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Updated: Jun 12, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

针对分布式神经成像数据的高效联合学习.

Bishal Thapaliya^1,2, Riyasat Ohib^1,3, Eloy Geenjaar^1,3

¹Translational Research In Neuroimaging and Data Science Center, Atlanta, GA, United States.

Frontiers in neuroinformatics

|September 24, 2024

概括

此摘要是机器生成的。

分散的稀疏联合学习 (FL) 可以在没有数据传输的情况下进行协作神经成像分析. 这种方法通过在当地培训稀疏的模型来提高效率和隐私,减少通信开支.

关键词:

沟通效率沟通效率沟通效率有效的联合学习.神经成像是一种神经成像.稀少的模型稀少的模型稀缺性是一种稀缺性.

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Last Updated: Jun 12, 2025

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科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
数据科学数据科学数据科学

背景情况:

神经成像研究越来越多地涉及数据共享.
由于隐私和问责问题,机构的数据控制阻碍了合作.
需要工具来分析分布式数据集,而无需直接传输数据.

研究的目的:

为分析合并的神经成像数据集提出一个分散的稀疏联合学习 (FL) 策略.
在协作研究中应对数据隐私,安全和机构控制方面的挑战.
为了减少联合学习框架中的通信开销.

主要方法:

开发了一个分散的稀疏联合学习 (FL) 战略.
强调对稀疏模型的本地训练,以最大限度地减少数据传输.
在客户端站点之间实施选择性共享模型参数.
使用青少年大脑认知发展 (ABCD) 数据集进行验证.

主要成果:

拟议的FL战略显著降低了通讯开支.
使用较大的模型和多样化的站点资源,效率的提高更为实质性.
在大型神经成像数据集上证明了该方法的有效性.

结论:

分散的稀疏FL为协作神经成像分析提供了有效的解决方案.
该方法在联合学习环境中提高了效率和可扩展性.
这种方法有助于跨机构对敏感数据进行安全和保护隐私的分析.