  In this article, we study the problem of robust reconfigurable intelligent
surface (RIS)-aided downlink communication over heterogeneous RIS types in the
supervised learning setting. By modeling downlink communication over
heterogeneous RIS designs as different workers that learn how to optimize phase
configurations in a distributed manner, we solve this distributed learning
problem using a distributionally robust formulation in a
communication-efficient manner, while establishing its rate of convergence. By
doing so, we ensure that the global model performance of the worst-case worker
is close to the performance of other workers. Simulation results show that our
proposed algorithm requires fewer communication rounds (about 50% lesser) to
achieve the same worst-case distribution test accuracy compared to competitive
baselines.
