Joint Selection of Local Trainers and Resource Allocation for Federated Learning in Open RAN Intelligent Controllers
Amardip Kumar Singh, Kim Khoa Nguyen
Abstract
Recently, Federated Learning (FL) has been applied in various research domains specially because of its privacy preserving and decentralized approach of model training. However, very few FL applications have been developed for the Radio Access Network (RAN) due to the lack of efficient deployment models. Open RAN (O-RAN) promises a high standard of meeting 5G services through its disaggregated, hierarchical, and distributed network function processing framework. Moreover, it comes with built-in intelligent controllers to instill smart decision making ability into RAN. In this paper, we propose a framework named O-RANFed to deploy and optimize FL tasks in O-RAN to provide 5G slicing services. To improve the performance of FL we formulate a joint mathematical optimization model of local learners selection and resource allocation to perform model training in every iteration. We solve this non-convex problem using the decomposition method. First, we propose a slicing based and deadline aware client selection algorithm. Then, we solve the reduced resource allocation problem by using successive convex approximation (SCA) method. Our simulation results show the proposed model outperforms the state-of-the-art FL methods such as FedAvg and FedProx in terms of convergence, learning time, and resource costs.