联邦式学习与帕雷托优化资源效率和快速模型在移动环境中的融合
June-Pyo Jung1, Young-Bae Ko1, Sung-Hwa Lim2
1Department of AI Convergence Network, Ajou Univeristy, 206, World Cup-ro, Suwon-si 16499, Republic of Korea.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
本研究介绍了一种资源效率高的联合学习 (FL) 方案,使用有偏见的客户端选择和层次的集群. 新方法显著减少了网络流量,并加速了模型的融合,以提高分布式学习的性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 联合学习 (FL) 能够在分散的数据上进行模型培训,从而保护用户的隐私.
- 现有的FL方法面临挑战,包括网络延迟,有限的用户设备资源和参数服务器拥堵.
- 这些局限性阻碍了资源的高效利用和模型的快速融合.
研究的目的:
- 提出一个新的资源效率高的联合学习计划.
- 解决传统FL在网络和计算资源消耗方面的局限性.
- 提高模型融合速度,降低网络负载.
主要方法:
- 实施了资源高效的FL方案,包括帕雷托最佳性和偏向客户选择.
- 使用了基于位置的分类以实现设备对设备 (D2D) 通信的层次结构.
- 使用的k-means集群用于高效的设备分组.
主要成果:
- 拟议的方案显著减少了分别的75.89%和78.77%的传输和接收网络流量,与0.75参与率的FedAvg相比.
- 与FedAvg和D2D-FedAvg相比,实现了更快的模型融合.
- 证明了高效的资源消耗和管理.
结论:
- 拟议的资源效率高的FL计划有效地减轻了网络拥堵和资源限制.
- 层次聚类和偏向的客户端选择提高了FL的性能和效率.
- 这种方法为大规模,资源有限的联合学习应用提供了有希望的解决方案.
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