Towards Native Support for Federated Learning in 6G

Mohammad Bariq Khan, Xueli An, Chenghui Peng

Abstract: 6G mobile communication system is expected to be intelligence inclusive, that is, its system architecture would be designed to provide AI services to anyone, anywhere, including the network system itself. This mandates a clean-slate approach to its design. In this paper, this broad question is addressed from the perspective of providing distributed learning services, more specifically, using the Federated Learning (FL) paradigm. The relationship between two network metrics and the FL performance in a hierarchical federated learning system is explored, in order to determine the new features that the network architecture needs to support. An estimation of the dependency of bandwidth requirements on different ML models is also provided.