FedORA: Resource Allocation for Federated Learning in ORAN using Radio Intelligent Controllers

TL;DR

This paper introduces FedORA, a resource allocation framework for federated learning in ORAN networks that uses reinforcement learning and predefined policies to optimize communication and energy efficiency in dynamic environments.

Contribution

It presents a novel two-stage optimization approach leveraging ORAN's modular architecture for dynamic RAT selection and resource allocation in federated learning.

Findings

Achieves low power consumption with competitive performance.

Enhances communication resilience in dynamic network environments.

Demonstrates scalability for real-time federated learning applications.

Abstract

This work proposes an integrated approach for optimising Federated Learning (FL) communication in dynamic and heterogeneous network environments. Leveraging the modular flexibility of the Open Radio Access Network (ORAN) architecture and multiple Radio Access Technologies (RATs), we aim to enhance data transmission efficiency and mitigate client-server communication constraints within the FL framework. Our system employs a two-stage optimisation strategy using ORAN's rApps and xApps. In the first stage, Reinforcement Learning (RL) based rApp is used to dynamically select each user's optimal Radio Access Technology (RAT), balancing energy efficiency with network performance. In the second stage, a model-based xApp facilitates near-real-time resource allocation optimisation through predefined policies to achieve optimal network performance. The dynamic RAT selection and resource allocation capabilities enabled by ORAN and multi-RAT contribute to robust communication resilience in dynamic network environments. Our approach demonstrates competitive performance with low power consumption compared to other state-of-the-art models, showcasing its potential for real-time applications demanding both accuracy and efficiency. This robust and comprehensive framework, enabling clients to utilise available resources effectively, highlights the potential for scalable, collaborative learning applications prioritising energy efficiency and network performance.