University of Oulu

C. B. Issaid, S. Samarakoon, M. Bennis and H. V. Poor, "Federated Distributionally Robust Optimization for Phase Configuration of RISs," 2021 IEEE Global Communications Conference (GLOBECOM), 2021, pp. 1-6, doi: 10.1109/GLOBECOM46510.2021.9685599.

Federated distributionally robust optimization for phase configuration of RISs

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Author: Issaid, Chaouki Ben1; Samarakoon, Sumudu1; Bennis, Mehdi1;
Organizations: 1Centre for Wireless Communications (CWC), University of Oulu, Finland
2Electrical Engineering Department, Princeton University, Princeton, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2023-01-03


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.

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ISBN: 978-1-7281-8104-2
ISBN Print: 978-1-7281-8105-9
Pages: 1 - 6
DOI: 10.1109/GLOBECOM46510.2021.9685599
Host publication: 2021 IEEE Global Communications Conference (GLOBECOM) : proceedings
Conference: IEEE Global Communications Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: 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.
EU Grant Number: (957218) IntellIoT - Intelligent, distributed, human-centered and trustworthy IoT environments
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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