  Federated learning (FL) is the most popular distributed machine learning
technique. However, implementation of FL over modern wireless networks faces
key challenges caused by (i) dynamics of the network conditions and (ii) the
coexistence of multiple FL services/tasks and other network services in the
system, which are not jointly considered in prior works. Motivated by these
challenges, we introduce a generic FL paradigm over NextG networks, called
dynamic multi-service FL (DMS-FL). We identify three unexplored design
considerations in DMS-FL: (i) FL service operator accumulation, (ii) wireless
resource fragmentation, and (iii) signal strength fluctuations. We take the
first steps towards addressing these design considerations by proposing a novel
distributed ML architecture called elastic virtualized FL (EV-FL). EV-FL
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
dimensions into existing FL architectures: (i) virtualization, (ii)
scalability, and (iii) elasticity. Through investigating EV-FL, we reveal a
series of open research directions for future work. We finally simulate EV-FL
to demonstrate its potential in saving wireless resources and increasing
fairness among FL services.
