  Federated learning (FL) with its data privacy protection and communication
efficiency has been considered as a promising learning framework for
beyond-5G/6G systems. We consider a scenario where a group of downlink non-FL
users are jointly served with a group of FL users using massive multiple-input
multiple-output technology. The main challenge is how to utilise the resource
to optimally serve both FL and non-FL users. We propose a communication scheme
that serves the downlink of the non-FL users (UEs) and the uplink of FL UEs in
each half of the frequency band. We formulate an optimization problem for
optimizing transmit power to maximize the minimum effective data rates for
non-FL users, while guaranteeing a quality-of-service time of each FL
communication round for FL users. Then, a successive convex approximation-based
algorithm is proposed to solve the formulated problem. Numerical results
confirm that our proposed scheme significantly outperforms the baseline scheme.
