Communication-efficient Federated Learning Design with Fronthaul Awareness in NG-RANs
Communication-efficient Federated Learning Design with Fronthaul Awareness in NG-RANs
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DOI：
10.1109/mass56207.2022.00089
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Ayman Younis;Chuanneng Sun;Dario Pompili
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文献类型：
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作者：
Ayman Younis;Chuanneng Sun;Dario Pompili
下一代无线接入网络(NG-RAN)通过将无线和计算功能分布式地推向终端用户，成为满足5G及以上应用的严格要求的一种很有前途的范例。随着新技术的出现、网络的增密化以及更丰富和更苛刻的应用，前端链路的有限容量和隐私问题对下一代网络系统在现实环境中的实现构成了严重的制约。为了应对这些挑战，我们提出了一种基于联邦学习(FL)的NG-RAN算法FedNG，在该算法中，用户设备(UE)和NG-RAN基础设施在整个学习过程中进行协作，并通过共享预测模型来确保隐私并减轻前端接口的负担。具体地说，通过将分布式单元的第一阶段训练模型作为局部训练的初始输入，然后将次优的分布式单元模型上传到中央单元以参与下一阶段的全局训练，我们提出的方案使得分布式单元能够协作地学习共享预测模型。最后，从准确度、服务延迟和流量大小三个方面对我们提出的方案进行了数值计算。我们提出的算法的收敛证实了我们的方法明显优于现有的基于联邦平均(FedAvg)的最新解决方案。
Next Generation Radio Access Networks (NG-RANs) have become a promising paradigm to meet the strict demands of the 5G and beyond applications by distributively pushing the radio and computing functionalities in the close approximate to end-users. With the emergence of new technologies, network densification, and richer and more demanding applications, the limited capacity of the fronthaul links and privacy concerns poses a severe constraint on realizing NG-RAN systems in the real environment. To tackle these challenges, we propose a Federated Learning (FL)-based NG-RAN algorithm, named FedNG, in which the User Equipment (UEs), as well as NG-RAN infrastructures, collaborate throughout the learning process and the sharing prediction model to ensure privacy and relieve the burden on fronthaul interface. Specifically, our proposed scheme enables Distributed Units (DUs) to cooperatively learn a shared predictive model by taking the first-phase training models of the DUs as the initial input of the local training and then uploading sub-optimal DU models to the Central Unit (CU) to involve in the next phase of global training. Finally, numerical results are provided to evaluate our proposed scheme in terms of accuracy, service latency, and traffic size. The convergence of our proposed algorithm confirms that our approach significantly outperforms the existing state-of-the-art solution based on Federated Averaging (FedAvg).