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.

翻译：智能设备（如手机、车辆、无人机）在感知、通信和计算能力方面的爆炸式增长，催生了前所未有的数据量。这些海量数据与机器学习技术的快速进步相结合，激发了多种智能应用。为了从数据中提炼出支持这些应用的智能，联邦学习作为一种有效的分布式机器学习框架应运而生，其潜在优势在于能够在网络边缘实现保护隐私的模型训练。本文从网络设计与资源编排两个角度，探讨了实现可扩展无线联邦学习的挑战与解决方案。在网络设计方面，我们讨论了面向任务的模型聚合如何影响无线联邦学习的性能，并提出有效的无线技术，通过降低模型聚合失真和提升设备参与度来增强通信可扩展性。在资源编排方面，我们指出了现有基于优化算法的局限性，并提出三种面向任务的学习算法，通过实现无线联邦学习的计算高效资源分配来增强算法可扩展性。最后，我们强调了几个值得进一步研究的重要潜在研究问题。