  Federated learning (FL) is a highly pursued machine learning technique that
can train a model centrally while keeping data distributed. Distributed
computation makes FL attractive for bandwidth limited applications especially
in wireless communications. There can be a large number of distributed edge
devices connected to a central parameter server (PS) and iteratively
download/upload data from/to the PS. Due to the limited bandwidth, only a
subset of connected devices can be scheduled in each round. There are usually
millions of parameters in the state-of-art machine learning models such as deep
learning, resulting in a high computation complexity as well as a high
communication burden on collecting/distributing data for training. To improve
communication efficiency and make the training model converge faster, we
propose a new scheduling policy and power allocation scheme using
non-orthogonal multiple access (NOMA) settings to maximize the weighted sum
data rate under practical constraints during the entire learning process. NOMA
allows multiple users to transmit on the same channel simultaneously. The user
scheduling problem is transformed into a maximum-weight independent set problem
that can be solved using graph theory. Simulation results show that the
proposed scheduling and power allocation scheme can help achieve a higher FL
testing accuracy in NOMA based wireless networks than other existing schemes.
