扩展开发智能虚拟环境 (FIVE) 的框架,包括用于模拟物联网设备的工件和基于动态网络共识的新去中心化联合学习
Miguel Rebollo1, Jaime Andrés Rincon2, Luís Hernández1
1Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, 46022 Valencia, Spain.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
本研究将基于共识的联合学习 (FL) 算法应用于无线临时网络 (WANET). 使用FIVE框架的模拟评估了代理的可达性,这对于在果园中部署自动拖拉机等系统至关重要.
科学领域:
- 分布式系统 分布式系统
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 联合学习 (FL) 是分布式机器学习的一个关键领域.
- 无线临时网络 (WANET) 为代理通信和模型共享带来了独特的挑战.
- 在部署自主系统之前,确保代理网络连接至关重要.
研究的目的:
- 将基于共识 (CoL) 的去中心化联合学习算法应用于WANETs.
- 通过现实的模拟来评估WANET中的代理可访问性.
- 在模拟框架中引入"工件"概念,以建模关键设备.
主要方法:
- 在WANET环境中实现基于共识的联合学习算法 (CoL).
- 使用Unity和SPADE代理开发一个现实的模拟框架 (FIVE).
- 案例研究涉及水果园中的自动拖拉机,其中包含用于连接的"工件".
主要成果:
- 在WANET中证明了COL算法的适用性.
- 验证用于模拟代理互动和网络连接的FIVE框架.
- 成功整合"工件"以表示基本的,特定于位置的设备.
结论:
- 该 CoL 算法适用于 WANET 中的分散学习.
- 五个框架为模拟复杂的基于代理的系统提供了一个强大的平台.
- "文物"概念是建模数字双胞胎和数字阴影的基本步骤.
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