The disaggregated and hierarchical architecture of Open Radio Access Network (ORAN) with openness paradigm promises to deliver the ever demanding 5G services. Meanwhile, it also faces new challenges for the efficient deployment of Machine Learning (ML) models. Although ORAN has been designed with built-in Radio Intelligent Controllers (RIC) providing the capability of training ML models, traditional centralized learning methods may be no longer appropriate for the RICs due to privacy issues, computational burden, and communication overhead. Recently, Federated Learning (FL), a powerful distributed ML training, has emerged as a new solution for training models in ORAN systems. 5G use cases such as meeting the network slice Service Level Agreement (SLA) and Key Performance Indicator (KPI) monitoring for the smart radio resource management can greatly benefit from the FL models. However, training FL models efficiently in ORAN system is a challenging issue due to the stringent deadline of ORAN control loops, expensive compute resources, and limited communication bandwidth. Moreover, to deliver Grade of Service (GoS), the trained ML models must converge with acceptable accuracy. In this paper, we propose a second order gradient descent based FL training method named MCORANFed that utilizes compression techniques to minimize the communication cost and yet converges at a faster rate than state-of-the-art FL variants. We formulate a joint optimization problem to minimize the overall resource cost and learning time, and then solve it by the decomposition method. Our experimental results prove that MCORANFed is communication efficient with respect to ORAN system, and outperforms FL methods like MFL, FedAvg, and ORANFed in terms of costs and convergence rate.

Amardip Kumar Singh

Kim Khoa Nguyen

IEEE/ACM Transactions on Networking

IEEE/ACM TRANSACTIONS ON NETWORKING 的高层次目标是发表来自通信/计算机网络领域的理论或实验探索的高质量原创研究成果，涵盖各种物理层技术上的各种信息传输网络，有线（各种引导媒体：例如，铜、光学）和无线（例如，射频、声学（例如，水下）、红外线），或这些的混合。该杂志欢迎报告新经验和实际系统实验的应用贡献。

The IEEE/ACM Transactions on Networking aims to publish high-quality original research results from the field of communication and computer networking. These studies can be theoretical or experimental, covering various physical layer technologies for information transmission networks, including wired networks (such as copper and optical media) and wireless networks (such as radio frequency, acoustic (e.g., underwater), and infrared), as well as hybrid applications of these technologies. The journal welcomes reports of new experiences and practical system experimental contributions.

IEEE/ACM Transactions on Networking 的目标是发表通信和计算机网络领域的高质量原创研究成果。这些研究可以是理论性的，也可以是实验性的，涵盖各种物理层技术的信息传输网络，包括有线网络（如铜缆和光纤）和无线网络（如射频、声学（例如水下声学）和红外线），以及这些技术的混合应用。该期刊欢迎报告新的经验和实际系统实验的应用贡献。

Category	Quartile
计算机科学	3
计算机科学, 计算机理论方法	2
计算机科学, 计算机硬件	3
计算机科学, 工程电子与电气	3
计算机科学, 电信学	3

Communication Efficient Compressed and Accelerated Federated Learning in Open RAN Intelligent Controllers

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