  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.
