Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing
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DOI：
10.1145/3453142.3491419
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Pinyarash Pinyoanuntapong;Tagore Pothuneedi;Ravikumar Balakrishnan;Minwoo Lee;Chen Chen-Chen;Pu Wang
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文献类型：
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作者：
Pinyarash Pinyoanuntapong;Tagore Pothuneedi;Ravikumar Balakrishnan;Minwoo Lee;Chen Chen-Chen;Pu Wang
在无线多跳线边缘计算网络（即Multi-Hop FL）上的联合学习（FL）是一种具有成本效益的分布在设备上的深度学习范式。本文介绍了一种基于高保真的基于Linux的模拟器FedEdge Simulator，该模拟器可实现快速原型制作，SIM卡到运行代码以及多跳FL系统的知识传输。 FedEdge Simulator建立在面向硬件的FedEdge实验框架的顶部，并具有逼真的物理层模拟器的新扩展。该模拟器利用基于跟踪的通道建模和动态链接计划，以最大程度地减少模拟器和物理测试台之间的现实差距。我们的最初实验证明了FedEdge模拟器的高保真度及其在强化学习中的SIM到现实知识转移方面的出色表现。
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.