# Elastic Federated Learning over Open Radio Access Network (O-RAN) for Concurrent Execution of Multiple Distributed Learning Tasks

arXiv: https://arxiv.org/abs/2305.02109 · HTML: https://mlsanity.com/paper/2305.02109/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 14 Apr 2023.

## Builds on (most cited first, 12 of 12)

- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing](https://mlsanity.com/paper/2007.09236/) (2020)
- [FedTP: Federated Learning by Transformer Personalization](https://mlsanity.com/paper/2211.01572/) (2022)
- [Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks](https://mlsanity.com/paper/2108.09103/) (2021)
- [Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks](https://mlsanity.com/paper/2202.02947/) (2022)
- [dApps: Distributed Applications for Real-time Inference and Control in O-RAN](https://mlsanity.com/paper/2203.02370/) (2022)
- [Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures](https://mlsanity.com/paper/2209.14171/) (2022)
- [Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks](https://mlsanity.com/paper/2011.12469/) (2020)
- [OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN](https://mlsanity.com/paper/2201.05632/) (2022)
- [Auction-Promoted Trading for Multiple Federated Learning Services in UAV-Aided Networks](https://mlsanity.com/paper/2206.05885/) (2022)
- [Optical Front/Mid-haul with Open Access-Edge Server Deployment Framework for Sliced O-RAN](https://mlsanity.com/paper/2110.09365/) (2021)

## Built on by (most cited first, 1 of 1)

- [Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection](https://mlsanity.com/paper/2404.06324/) (2024)

## Abstract (arXiv)

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 resources and perspective-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 called elastic FL (EFL). EFL unleashes 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 prototype EFL to demonstrate its potential in improving the performance of FL-services.