适应性数据集管理方案用于移动边缘计算中的轻量级联合学习
Jingyeom Kim1, Juneseok Bang1, Joohyung Lee1
1School of Computing, Gachon University, Seongnam 13120, Republic of Korea.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
由于计算能力有限,移动设备上的联合学习 (FL) 面临着挑战. 本研究引入了一个自适应数据集管理 (ADM) 方案,以减少当地培训负担,改善对物联网的参与.
科学领域:
- 机器学习 机器学习
- 移动边缘计算 移动边缘计算
- 物联网的物联网,就是物联网.
背景情况:
- 联合学习 (FL) 允许跨移动设备 (MD) 的协作模式培训,而无需数据暴露.
- FL减轻了中央服务器的负担,但对能力有限的MDs强加了大量的本地培训计算.
研究的目的:
- 提出一个适应性数据集管理 (ADM) 计划,以减少FLMD的当地培训负担.
- 应对MDs有限的计算能力的挑战,阻碍其对FL的贡献.
主要方法:
- 实证研究数据集大小对沟通轮的准确性收益的影响.
- 引入一个折扣因子,表示数据集大小对准确性的减少影响.
- 为ADM问题制定理论框架,考虑折扣因子和Kullback-Leibler分歧 (KLD).
- 基于贪的启发式算法的建议,以解决非凸的ADM优化问题.
主要成果:
- 证实数据集大小对FL的准确性收益的影响正在减少.
- 拟议的ADM计划有效地减少了MD的培训负担.
- 启发式算法提供了一个低复杂度的亚最佳解决方案.
结论:
- 该ADM计划成功地减轻了佛罗里达州MD的当地培训负担.
- 保持可接受的训练准确度,同时降低移动设备的计算需求.
- 该方法提高了在资源有限的物联网环境中FL的可行性.
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