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Federated learning (FL) is the most popular distributed machine learningtechnique. However, implementation of FL over modern wireless networks faceskey challenges caused by (i) dynamics of the network conditions and (ii) thecoexistence of multiple FL services/tasks and other network services in thesystem, which are not jointly considered in prior works. Motivated by thesechallenges, we introduce a generic FL paradigm over NextG networks, calleddynamic multi-service FL (DMS-FL). We identify three unexplored designconsiderations in DMS-FL: (i) FL service operator accumulation, (ii) wirelessresource fragmentation, and (iii) signal strength fluctuations. We take thefirst steps towards addressing these design considerations by proposing a noveldistributed ML architecture called elastic virtualized FL (EV-FL). EV-FLunleashes the full potential of Open RAN (O-RAN) systems and introduces anelastic resource provisioning methodology to execute FL services. It furtherconstitutes a multi-time-scale FL management system that introduces threedimensions into existing FL architectures: (i) virtualization, (ii)scalability, and (iii) elasticity. Through investigating EV-FL, we reveal aseries of open research directions for future work. We finally simulate EV-FLto demonstrate its potential in saving wireless resources and increasingfairness among FL services.

Synergies Between Federated Learning and O-RAN: Towards an Elastic Virtualized Architecture for Multiple Distributed Machine Learning Services