Modeling and Analysis of Intermittent Federated Learning Over Cellular-Connected UAV Networks

no code implementations • 13 Oct 2021 • Chun-Hung Liu, Di-Chun Liang, Rung-Hung Gau, Lu Wei

Federated learning (FL) is a promising distributed learning technique particularly suitable for wireless learning scenarios since it can accomplish a learning task without raw data transportation so as to preserve data privacy and lower network resource consumption.

Federated Learning

Coverage Analysis for Dense Heterogeneous Networks with Cooperative NOMA

no code implementations • 14 Aug 2020 • Chun-Hung Liu, Di-Chun Liang, Po-Chia Chen, Jie-Ru Yang

In a heterogeneous cellular network (HetNet) consisting of $M$ tiers of densely-deployed base stations (BSs), consider that each of the BSs in the HetNet that are associated with multiple users is able to simultaneously schedule and serve two users in a downlink time slot by performing the (power-domain) non-orthogonal multiple access (NOMA) scheme.