New Paper Accepted

Our paper Federated Distributionally Robust Optimization for Phase Configuration of RISs, joint work with Sumudu Samarakoon , Mehdi Bennis, and H. Vincent Poor has been accepted in 2021 IEEE Global Communications Conference (GLOBECOM).


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.

Chaouki ben Issaid
Chaouki ben Issaid
Postdoctoral Fellow Researcher