通信效率高的混合联合学习用于电子健康,具有水平和垂直数据分区
IEEE transactions on neural networks and learning systems
|April 10, 2024
概括
本研究介绍了电子医疗 (电子健康) 数据的混合联合学习框架. 这种新的方法提高了人工智能驱动的医疗诊断与分区数据的通信效率和准确性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 医疗保健信息学 医疗保健信息学
背景情况:
- 电子医疗 (e-health) 通过整合智能设备和机构的数据来利用人工智能进行诊断.
- 联合学习解决了电子健康中的通信和隐私问题,但在横向和垂直分割的医疗数据方面存在困难.
- 现有的水平联合学习 (HFL) 和垂直联合学习 (VFL) 方法对于复杂的数据分割是不够的,导致高通信成本和精度降低.
研究的目的:
- 开发HFL和VFL的有效集成,以提高电子卫生中的沟通效率.
- 克服纯粹的HFL/VFL组合的局限性,包括培训效率低和融合问题.
- 为人工智能驱动的医疗诊断提供一个强大的框架,使用水平和垂直分区的数据.
主要方法:
- 提出了一个混合联合学习框架,其中有中间结果交换和两个聚合阶段.
- 在框架内开发了一个混合随机梯度下降 (HSGD) 算法用于模型训练.
- 理论上分析了HSGD算法的合上限,并设计了对参数调整和数据传输减少的自适应策略.
主要成果:
- 拟议的HSGD算法实现了所需的准确性,同时大大降低了通信成本.
- 实验结果验证了混合联合学习框架和适应性策略的有效性.
- 与现有方法相比,证明了培训效率和趋同的提高.
结论:
- 综合混合联合学习方法有效地处理横向和垂直分区的电子健康数据.
- HSGD算法为人工智能驱动的医学诊断提供了一种沟通效率高,准确的解决方案.
- 适应性策略提高模型性能,减少数据传输,为实际的电子健康应用铺平了道路.
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