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Federated learning (FL) is a popular distributed machine learning (ML)technique in Internet of Things (IoT) networks, where resource-constraineddevices collaboratively train ML models while preserving data privacy. However,implementation of FL over 5G-and-beyond wireless networks faces key challengescaused by (i) dynamics of the wireless network conditions and (ii) thecoexistence of multiple FL-services in the system. In this paper, we unveil twokey phenomena that arise from these challenges: over/under-provisioning ofresources and perspective-driven load balancing, both of which significantlyimpact FL performance in IoT environments. We take the first steps towardsaddressing these phenomena by proposing a novel distributed ML architecturecalled elastic FL (EFL). EFL unleashes the full potential of Open RAN (O-RAN)systems and introduces an elastic resource provisioning methodology to executeFL-services. It further constitutes a multi-time-scale FL management systemthat introduces three dedicated network control functionalities tailored forFL-services, including (i) non-real-time (non-RT) system descriptor, whichtrains ML-based applications to predict both system and FL-related dynamics andparameters; (ii) near-RT FL controller, which handles O-RAN slicing andmobility management for the seamless execution of FL-services; (iii) FL MACscheduler, which conducts real-time resource allocation to the end clients ofvarious FL-services. We finally prototype EFL to demonstrate its potential inimproving the performance of FL-services.

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