  As the 5G communication networks are being widely deployed worldwide, both
industry and academia have started to move beyond 5G and explore 6G
communications. It is generally believed that 6G will be established on
ubiquitous Artificial Intelligence (AI) to achieve data-driven Machine Learning
(ML) solutions in heterogeneous and massive-scale networks. However,
traditional ML techniques require centralized data collection and processing by
a central server, which is becoming a bottleneck of large-scale implementation
in daily life due to significantly increasing privacy concerns. Federated
learning, as an emerging distributed AI approach with privacy preservation
nature, is particularly attractive for various wireless applications,
especially being treated as one of the vital solutions to achieve ubiquitous AI
in 6G. In this article, we first introduce the integration of 6G and federated
learning and provide potential federated learning applications for 6G. We then
describe key technical challenges, the corresponding federated learning
methods, and open problems for future research on federated learning in the
context of 6G communications.
