  We advocate a new resource allocation framework, which we term resource
rationing, for wireless federated learning (FL). Unlike existing resource
allocation methods for FL, resource rationing focuses on balancing resources
across learning rounds so that their collective impact on the federated
learning performance is explicitly captured. This new framework can be
integrated seamlessly with existing resource allocation schemes to optimize the
convergence of FL. In particular, a novel "later-is-better" principle is at the
front and center of resource rationing, which is validated empirically in
several instances of wireless FL. We also point out technical challenges and
research opportunities that are worth pursuing. Resource rationing highlights
the benefits of treating the emerging FL as a new class of service that has its
own characteristics, and designing communication algorithms for this particular
service.
