Federated Learning (FL) over wireless multi-hop edge computing networks, i.e., multi-hop FL, is a cost-effective distributed on-device deep learning paradigm. This paper presents FedEdge simulator, a high-fidelity Linux-based simulator, which enables fast prototyping, sim-to-real code, and knowledge transfer for multi-hop FL systems. FedEdge simulator is built on top of the hardware-oriented FedEdge experimental framework with a new extension of the realistic physical layer emulator. This emulator exploits trace-based channel modeling and dynamic link scheduling to minimize the reality gap between the simulator and the physical testbed. Our initial experiments demonstrate the high fidelity of the FedEdge simulator and its superior performance on sim-to-real knowledge transfer in reinforcement learning-optimized multi-hop FL.
翻译:联邦学习(FL)对无线多霍边缘计算网络,即多霍、多霍、多霍、多卢,是一个具有成本效益的分布式深层学习模式。本文展示了FedEdge模拟器、一个基于高纤维的Linux模拟器,它能为多霍、多博、多博、多视、多视、多视、多视、多视、多视、多视、多视、多视、多视、多视、多视、新扩展现实物理层模拟器的实验框架之上,FedEdge模拟器可以建立起来。这个模拟器利用基于追踪的频道模型和动态链接列表,以尽量减少模拟器和物理测试台之间的现实差距。我们的初步实验显示,FedEdge模拟器具有高度的忠实性,及其在加强学习-优化多视、多视、多视、多视、模拟的模拟器方面,在模拟、模拟、虚拟知识转让方面表现优异性。