A Survey on Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks
Wireless networks are increasingly relying on machine learning (ML) paradigms to provide various services at the user level. Yet, it remains impractical for users to offload their collected data set to a cloud server for centrally training their local ML model. Federated learning (FL), which aims to...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10474098/ |
| _version_ | 1797222005432385536 |
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| author | Sree Krishna Das Benoit Champagne Ioannis Psaromiligkos Yunlong Cai |
| author_facet | Sree Krishna Das Benoit Champagne Ioannis Psaromiligkos Yunlong Cai |
| author_sort | Sree Krishna Das |
| collection | DOAJ |
| description | Wireless networks are increasingly relying on machine learning (ML) paradigms to provide various services at the user level. Yet, it remains impractical for users to offload their collected data set to a cloud server for centrally training their local ML model. Federated learning (FL), which aims to collaboratively train a global ML model by leveraging the distributed wireless computation resources across users without exchanging their local information, is therefore deemed as a promising solution for enabling intelligent wireless networks in the data-driven society of the future. Recently, reconfigurable intelligent metasurfaces (RIMs) have emerged as a revolutionary technology, offering a controllable means for increasing signal diversity and reshaping transmission channels, without implementation constraints traditionally associated with multi-antenna systems. In this paper, we present a comprehensive survey of recent works on the applications of FL to RIM-aided communications. We first review the fundamental basis of FL with an emphasis on distributed learning mechanisms, as well as the operating principles of RIMs, including tuning mechanisms, operation modes, and deployment options. We then proceed with an in-depth survey of literature on FL-based approaches recently proposed for the solution of three key interrelated problems in RIM-aided wireless networks, namely: channel estimation (CE), passive beamforming (PBF) and resource allocation (RA). In each case, we illustrate the discussion by introducing an expanded FL (EFL) framework in which only a subset of active users partake in the distributed training process, thereby allowing to reduce transmission overhead. Lastly, we discuss some current challenges and promising research avenues for leveraging the full potential of FL in future RIM-aided extremely large-scale multiple-input-multiple-output (XL-MIMO) networks. |
| first_indexed | 2024-04-24T13:14:27Z |
| format | Article |
| id | doaj.art-e74ebd0b4d9e4cf29af626a00abff17f |
| institution | Directory Open Access Journal |
| issn | 2644-125X |
| language | English |
| last_indexed | 2024-04-24T13:14:27Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj.art-e74ebd0b4d9e4cf29af626a00abff17f2024-04-04T23:00:46ZengIEEEIEEE Open Journal of the Communications Society2644-125X2024-01-0151846187910.1109/OJCOMS.2024.337826610474098A Survey on Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless NetworksSree Krishna Das0https://orcid.org/0009-0004-9146-2346Benoit Champagne1https://orcid.org/0000-0002-0022-6072Ioannis Psaromiligkos2https://orcid.org/0000-0002-1643-5143Yunlong Cai3https://orcid.org/0000-0001-9418-1700Department of Electrical and Computer Engineering, McGill University, Montreal, CanadaDepartment of Electrical and Computer Engineering, McGill University, Montreal, CanadaDepartment of Electrical and Computer Engineering, McGill University, Montreal, CanadaCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, ChinaWireless networks are increasingly relying on machine learning (ML) paradigms to provide various services at the user level. Yet, it remains impractical for users to offload their collected data set to a cloud server for centrally training their local ML model. Federated learning (FL), which aims to collaboratively train a global ML model by leveraging the distributed wireless computation resources across users without exchanging their local information, is therefore deemed as a promising solution for enabling intelligent wireless networks in the data-driven society of the future. Recently, reconfigurable intelligent metasurfaces (RIMs) have emerged as a revolutionary technology, offering a controllable means for increasing signal diversity and reshaping transmission channels, without implementation constraints traditionally associated with multi-antenna systems. In this paper, we present a comprehensive survey of recent works on the applications of FL to RIM-aided communications. We first review the fundamental basis of FL with an emphasis on distributed learning mechanisms, as well as the operating principles of RIMs, including tuning mechanisms, operation modes, and deployment options. We then proceed with an in-depth survey of literature on FL-based approaches recently proposed for the solution of three key interrelated problems in RIM-aided wireless networks, namely: channel estimation (CE), passive beamforming (PBF) and resource allocation (RA). In each case, we illustrate the discussion by introducing an expanded FL (EFL) framework in which only a subset of active users partake in the distributed training process, thereby allowing to reduce transmission overhead. Lastly, we discuss some current challenges and promising research avenues for leveraging the full potential of FL in future RIM-aided extremely large-scale multiple-input-multiple-output (XL-MIMO) networks.https://ieeexplore.ieee.org/document/10474098/6Gfederated learningreconfigurable intelligent metasurfaceschannel estimationpassive beamformingresource allocation |
| spellingShingle | Sree Krishna Das Benoit Champagne Ioannis Psaromiligkos Yunlong Cai A Survey on Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks IEEE Open Journal of the Communications Society 6G federated learning reconfigurable intelligent metasurfaces channel estimation passive beamforming resource allocation |
| title | A Survey on Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks |
| title_full | A Survey on Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks |
| title_fullStr | A Survey on Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks |
| title_full_unstemmed | A Survey on Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks |
| title_short | A Survey on Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks |
| title_sort | survey on federated learning for reconfigurable intelligent metasurfaces aided wireless networks |
| topic | 6G federated learning reconfigurable intelligent metasurfaces channel estimation passive beamforming resource allocation |
| url | https://ieeexplore.ieee.org/document/10474098/ |
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