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（Federated Learning, FL）是目前最流行的分布式机器学习技术。然而，在现代无线网络中部署FL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FL服务/任务与其他网络服务的共存问题。现有研究尚未充分考虑这些挑战。受此启发，我们提出了一种面向下一代（NextG）网络的通用FL范式，称为动态多服务FL（DMS-FL）。我们识别出DMS-FL中三个尚未探索的设计考量：（i）FL服务运营者累积、（ii）无线资源碎片化，以及（iii）信号强度波动。为应对这些设计考量，我们首次提出一种名为弹性虚拟化FL（EV-FL）的新型分布式机器学习架构。EV-FL充分释放了开放无线接入网（O-RAN）系统的潜力，并引入了一种弹性资源供应方法来执行FL服务。该架构进一步构建了一个多时间尺度FL管理系统，为现有FL架构引入了三个维度：（i）虚拟化、（ii）可扩展性，以及（iii）弹性。通过研究EV-FL，我们揭示了一系列未来工作的开放研究方向。最后，我们通过仿真验证了EV-FL在节省无线资源和提升FL服务公平性方面的潜力。