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.

翻译：联邦学习是当前最主流的分布式机器学习技术。然而，联邦学习在现代无线网络中的部署面临两大关键挑战：(i)网络条件的动态性以及(ii)系统中多个联邦学习服务/任务与其他网络服务的共存问题，而现有研究均未综合考虑这些因素。针对这些挑战，我们提出了面向下一代网络的通用联邦学习范式——动态多服务联邦学习(DMS-FL)。该范式揭示了三个未被探索的设计要素：(i)联邦学习服务运营商积累、(ii)无线资源碎片化及(iii)信号强度波动。通过提出名为弹性虚拟化联邦学习(EV-FL)的新型分布式机器学习架构，我们率先着手解决这些设计问题。EV-FL充分发挥开放无线接入网(O-RAN)系统的潜力，引入弹性资源供给方法来执行联邦学习服务，并构建了包含三个维度的多时间尺度联邦学习管理系统：(i)虚拟化、(ii)可扩展性及(iii)弹性。通过对EV-FL的深入研究，我们揭示了一系列未来研究的开放方向。最后通过仿真验证了EV-FL在节约无线资源与提升联邦学习服务公平性方面的潜力。