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对基于无线IoMT的医疗保健系统的长尾数据进行辅助联合学习的评分
IEEE journal of biomedical and health informatics
|August 2, 2023
概括
这项研究引入了一个新的联合学习 (FL) 框架,用于医疗物联网 (IoMT) 系统. 它通过智能选择客户进行培训来提高不平衡数据集和不可靠的无线通道上的模型准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 无线医疗物联网 (IoMT) 系统中的联合学习 (FL) 面临着长尾数据的挑战,导致偏见的全球模型.
- 严重的无线通道色可以将移动客户端排除在模型聚合之外,进一步降低FL性能.
研究的目的:
- 提出一个新的得分辅助FL框架,以解决IoMT医疗保健系统中的数据不平衡和不可靠的无线条件.
- 通过选择具有更多尾部数据和更好的传输质量的移动客户端来提高FL的性能.
主要方法:
- 建议采用基于逻辑的评分客户端选择方法,通过探索本地数据分布来减轻长尾数据的影响.
- 频道状态信息 (CSI) 和数据速率被纳入客户选择的评分机制,解决无线色问题.
- 开发了一种结合逻辑和模型上传率的新型客户端选择方法,以优化FL性能.
主要成果:
- 与传统的FedAvg相比,拟议的框架显著提高了FL的性能.
- 在CIFAR-10-LT数据集中,在不平衡因子为50的情况下,获得了从4.44%到28.36%的精度增长.
- 基于分数的抽样策略有效地选择具有有价值的尾部数据和稳定的传输条件的客户.
结论:
- 新的评分辅助FL框架有效地克服了 IoMT 系统中长尾数据和无线通道色所带来的挑战.
- 提出的客户选择方法提高了医疗保健应用中的模型准确性和稳定性.
- 这项工作有助于更可靠,更有效的联合学习医疗应用.
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