在无人机网络中的节能层次联合学习,在非形损失下进行部分AI模型上传
Hui Li1, Shiyu Wang2,3, Yu Du2,3
1College of Information Engineering, Taizhou University, Taizhou 225300, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究介绍了用于移动网络的节能等级联合学习 (HFL) 方案. 它优化模型上传以减少能源消耗和通信成本,同时保持学习准确性.
科学领域:
- 移动计算 移动计算
- 人工智能的人工智能
- 网络通讯 网络通讯 网络通讯
背景情况:
- 层次联合学习 (HFL) 在移动场景中面临挑战,原因是高能耗,通信成本和融合问题.
- 现有的HFL方法假定形损失和全参数上传,这对于现实世界非形任务和边缘约束来说是不切实际的.
研究的目的:
- 建议在非凸损失下在移动/无人机网络中对HFL进行能源效率优化方案.
- 通过整合动态部分维度模型上传和最大限度地减少总能耗来解决当前HFL的局限性.
主要方法:
- 通过选关键更新维度开发了一个动态调节的部分维度模型上传机制.
- 构建了一个总能耗最小化模型,包括通信/计算能耗和出席率约束.
- 利用利亚普诺夫优化方法将长期问题转化为单回合可解决的子问题.
主要成果:
- 与最初的HFL算法相比,拟议的方案大大降低了能源消耗.
- 保持了高的测试准确度,证明了节能方法的有效性.
- 移动性对系统性能产生积极影响,通过模拟结果进行验证.
结论:
- 新的HFL方案有效地平衡了移动环境中的能源效率和学习性能.
- 部分维度模型上传对于在资源有限的移动/无人机网络中减少开销至关重要.
- 整合移动性考虑因素提高了联合学习系统的实用性和性能.
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