  This work studies federated learning (FL) over a fog radio access network, in
which multiple internet-of-things (IoT) devices cooperatively learn a shared
machine learning model by communicating with a cloud server (CS) through
distributed access points (APs). Under the assumption that the fronthaul links
connecting APs to CS have finite capacity, a rate-splitting transmission at IoT
devices (IDs) is proposed which enables hybrid edge and cloud decoding of split
uplink messages. The problem of completion time minimization for FL is tackled
by optimizing the rate-splitting transmission and fronthaul quantization
strategies along with training hyperparameters such as precision and iteration
numbers. Numerical results show that the proposed rate-splitting transmission
achieves notable gains over benchmark schemes which rely solely on edge or
cloud decoding.
