The explosive growth of smart devices (e.g., mobile phones, vehicles, drones)
with sensing, communication, and computation capabilities gives rise to an
unprecedented amount of data. The generated massive data together with the
rapid advancement of machine learning (ML) techniques spark a variety of
intelligent applications. To distill intelligence for supporting these
applications, federated learning (FL) emerges as an effective distributed ML
framework, given its potential to enable privacy-preserving model training at
the network edge. In this article, we discuss the challenges and solutions of
achieving scalable wireless FL from the perspectives of both network design and
resource orchestration. For network design, we discuss how task-oriented model
aggregation affects the performance of wireless FL, followed by proposing
effective wireless techniques to enhance the communication scalability via
reducing the model aggregation distortion and improving the device
participation. For resource orchestration, we identify the limitations of the
existing optimization-based algorithms and propose three task-oriented learning
algorithms to enhance the algorithmic scalability via achieving
computation-efficient resource allocation for wireless FL. We highlight several
potential research issues that deserve further study.

通过网络设计和资源编排的两个方面讨论了实现可扩展无线联邦学习的挑战和解决方案，并提出了三种面向任务的学习算法来提高算法的可扩展性，实现对无线联邦学习的计算高效资源分配。