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.

翻译：在本篇文章中,我们研究了在受监督的学习环境中,对不同类型的RIS类型进行强力的可调整智能表面(RIS)辅助下链路通信的问题。通过对不同的RIS设计进行下链路通信的建模,将不同的RIS设计作为不同的工人,学习如何以分布方式优化阶段配置,我们用一种分布式强的公式,以通信效率的方式解决这种分散式学习问题,同时确定其趋同率。我们这样做,确保最坏的工人的全球模型性能接近其他工人的业绩。模拟结果显示,我们提议的算法要求较少的通信周期(比竞争性基线低约50%),以达到与竞争基线相同的最坏的分布测试精确度。