Elastic Federated Learning Over Open Radio Access Network (O-RAN) for Concurrent Execution of Multiple Distributed Learning Tasks
Payam Abdisarabshali, Nicholas Accurso, Filippo Malandra, Weifeng Su, Seyyedali Hosseinalipour · IEEE Internet of Things Magazine · 2025
Federated learning (FL) is a popular distributed machine learning (ML) technique in Internet of Things (IoT) networks, where resource-constrained devices collaboratively train ML models while preserving data privacy. However, implementation of FL over 5G-and-beyond wireless networks faces key challenges caused by (i) dynamics of the wireless network conditions and (ii) the coexistence of multiple FL-services in the system. In this paper, we unveil two key phenomena that arise from these challenges:over/under-provisioning of resourcesandperspective-driven load balancing, both of which significantly impact FL performance in IoT environments. We take the first steps towards addressing these phenomena by proposing a novel distributed ML architecture calledelastic FL(EFL).EFLunleashes the full potential of Open RAN (O-RAN) systems and introduces an elastic resource provisioning methodology to execute FL-services. It further constitutes a multi-time-scale FL management system that introduces three dedicated network control functionalities tailored for FL-services, including(i) non-real-time (non-RT) system descriptor, which trains ML-based applications to predict both system and FL-related dynamics and parameters;(ii) near-RT FL controller, which handles O-RAN slicing and mobility management for the seamless execution of FL-services;(iii) FL MAC scheduler, which conducts real-time resource allocation to the end clients of various FL-services. We finally prototypeEFLto demonstrate its potential in improving the performance of FL-services. Our implementations are publicly available at the following GitHub repository:https://github.com/payamsiabd/Elastic_FL_Over_O_RAN.git