TY - GEN
T1 - Federated Distributionally Robust Optimization for Phase Configuration of RISs
AU - Issaid, Chaouki Ben
AU - Samarakoon, Sumudu
AU - Bennis, Mehdi
AU - Poor, H. Vincent
N1 - Funding Information:
This work is supported by Academy of Finland 6G Flagship (grant no. 318927) and project SMARTER, projects EU-ICT IntellIoT and EUCHIS-TERA LearningEdge, and CONNECT, Infotech-NOOR, and NEGEIN.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Reconfigurable intelligent surface (RIS)
KW - communication-efficiency
KW - distributionally robust optimization (DRO)
KW - federated learning
UR - http://www.scopus.com/inward/record.url?scp=85127280748&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85127280748&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM46510.2021.9685599
DO - 10.1109/GLOBECOM46510.2021.9685599
M3 - Conference contribution
AN - SCOPUS:85127280748
T3 - 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
BT - 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Global Communications Conference, GLOBECOM 2021
Y2 - 7 December 2021 through 11 December 2021
ER -