2 research outputs found
Towards Ubiquitous AI in 6G with Federated Learning
With 5G cellular systems being actively deployed worldwide, the research
community has started to explore novel technological advances for the
subsequent generation, i.e., 6G. It is commonly believed that 6G will be built
on a new vision of ubiquitous AI, an hyper-flexible architecture that brings
human-like intelligence into every aspect of networking systems. Despite its
great promise, there are several novel challenges expected to arise in
ubiquitous AI-based 6G. Although numerous attempts have been made to apply AI
to wireless networks, these attempts have not yet seen any large-scale
implementation in practical systems. One of the key challenges is the
difficulty to implement distributed AI across a massive number of heterogeneous
devices. Federated learning (FL) is an emerging distributed AI solution that
enables data-driven AI solutions in heterogeneous and potentially massive-scale
networks. Although it still in an early stage of development, FL-inspired
architecture has been recognized as one of the most promising solutions to
fulfill ubiquitous AI in 6G. In this article, we identify the requirements that
will drive convergence between 6G and AI. We propose an FL-based network
architecture and discuss its potential for addressing some of the novel
challenges expected in 6G. Future trends and key research problems for
FL-enabled 6G are also discussed.Comment: Submitted to IEEE Communication Magazin
Federated Learning for 6G Communications: Challenges, Methods, and Future Directions
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