George Levis

FedRA: A Fully Decentralized Federated Learning Framework for Robust Intelligence Sharing across O-RAN dApps

Sotirios T. Spantideas, Anastasios E. Giannopoulos, George A. Levis, Panagiotis Trakadas

We propose a resilient and fully decentralized federated learning framework specifically adapted for Open Radio Access Networks (O-RAN) named FedRA that enables collaborative intelligence between dApps. We present how FedRA can be deployed in the O-RAN architecture by using already existing interfaces between the well-defined RAN components and how real-time ML applications (dApps) can interact to share their distilled intelligence. The software components of FedRA are described along with the step-by-step deployment workflow, outlining practical guidelines for its implementation. Finally, the framework is validated in a simulated environment using both real and simulated datasets reporting the time series of throughput provided by multiple radio units; two different ML models hosted in FedRA nodes either detect anomalies in upcoming network traffic or forecast data-rate values. The achieved accuracy confirms the validity of FedRA in the training of cell-specific ML models and the decentralized collaborative intelligence sharing among them. FedRA is also compared to a typical client/server approach in terms of model accuracy and network communication cost.