With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge. While still at an early age, it is expected that the evolving 6G network will adopt advanced artificial intelligence (AI) technologies to collect, transmit, and learn this valuable data for innovative applications and intelligent services. However, traditional machine learning (ML) approaches require centralizing the training data in the data center or cloud, raising serious user-privacy concerns. Federated learning, as an emerging distributed AI paradigm with privacy-preserving nature, is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks. However, there are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks. In this article, we investigate the optimization approaches that can effectively address the challenging heterogeneity issues from three aspects: incentive mechanism design, network resource management, and personalized model optimization. We also present some open problems and promising directions for future research.

翻译：随着5G网络的快速发展，网络边缘产生了数十亿智能物联网设备及海量数据。尽管仍处于早期阶段，但预计演进中的6G网络将采用先进人工智能技术来收集、传输和学习这些宝贵数据，以支持创新应用与智能服务。然而，传统机器学习方法需要将训练数据集中至数据中心或云端，这引发了严重的用户隐私问题。联邦学习作为一种新兴的分布式AI范式，具有隐私保护特性，有望成为实现6G网络泛在AI的关键使能技术。然而，在6G网络中高效实施联邦学习面临系统和统计异构性等多重挑战。本文从激励机制设计、网络资源管理和个性化模型优化三个方面，系统研究了有效应对异构性挑战的优化方法，并提出了未来研究中值得关注的开放问题与可行方向。