# Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing

arXiv: https://arxiv.org/abs/2110.08952 · HTML: https://mlsanity.com/paper/2110.08952/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer](https://mlsanity.com/paper/2011.03148/) (2020)
- [Generative Neural Network Channel Modeling for Millimeter-Wave UAV Communication](https://mlsanity.com/paper/2012.09133/) (2020)
- [The Road to BOFUSS: The Basic OpenFlow User-space Software Switch](https://mlsanity.com/paper/1901.06699/) (2019)
- [EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge](https://mlsanity.com/paper/2111.09410/) (2021)

## Abstract (arXiv)

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.