Federated Learning for 6G HetNets' Physical Layer Optimization: Perspectives, Trends, and Challenges

Bartsiokas, Ioannis; Gkonis, Panagiotis; Papazafeiropoulos, Anastasios; Kaklamani, Dimitra; Venieris, Iakovos

dc.contributor.author	Bartsiokas, Ioannis
dc.contributor.author	Gkonis, Panagiotis
dc.contributor.author	Papazafeiropoulos, Anastasios
dc.contributor.author	Kaklamani, Dimitra
dc.contributor.author	Venieris, Iakovos
dc.date.accessioned	2023-10-17T19:27:55Z
dc.date.available	2023-10-17T19:27:55Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/58199
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.25895
dc.rights	Αναφορά Δημιουργού-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nd/3.0/gr/	*
dc.subject	6G	en
dc.subject	Cell-Free Massive MIMO	en
dc.subject	Centralized Learning	en
dc.subject	Federated Learning	en
dc.subject	machine learning	en
dc.subject	Non-Orthogonal Multiple Access	en
dc.subject	Physical Layer	en
dc.subject	Radio Resource Management	en
dc.subject	Reconfigurable Intelligent Surface	en
dc.title	Federated Learning for 6G HetNets' Physical Layer Optimization: Perspectives, Trends, and Challenges	en
heal.type	bookChapter
heal.classification	Telecommunications	el
heal.contributorName	Bartsiokas, Ioannis
heal.contributorName	Gkonis, Panagiotis
heal.contributorName	Papazafeiropoulos, Anastasios
heal.contributorName	Kaklamani, Dimitra
heal.contributorName	Venieris, Iakovos
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2025-01-01
heal.bibliographicCitation	I. A. Bartsiokas, P. K. Gkonis, A.K. Papazafeiropoulos, D. I. Kaklamani and I. S. Venieris, "Federated Learning for 6G HetNets’ Physical Layer Optimization: Perspectives, Trends, and Challenges," Encyclopedia of Information Science and Technology, Sixth Edition, IGI Global, 2025, pp. 1-28, doi: 10.4018/978-1-6684-7366-5.ch070.	en
heal.abstract	This chapter presents a survey that focuses on the implementation of federated learning (FL) techniques in sixth generation (6G) networks’ physical layer (PHY) to meet the increasing user requirements. FL in PHY perspectives are discussed, along with the current trends and the present challenges in order to deploy efficient (cost, energy, spectral, computational) FL models for PHY tasks. Moreover, the utilization of FL methods is, also, discussed when channel state information (CSI) is not guaranteed in a 6G scenario. In such conditions, the joint use of cell free (CF) massive multiple-input-multiple-output (mMIMO), reconfigurable intelligent surfaces (RIS), and non-orthogonal multiple access (NOMA) and FL methods is proposed. Finally, an FL-based scheme for relay node (RN) placement in 6G networks is presented as an indicative use case for FL utilization in modern era networks. Results indicate that the proposed FL scheme overperforms state-of-the-art centralized learning schemes concerning the tradeoff between machine learning (ML) metrics maximization and training latency.	en
heal.publisher	IGI Global	el
heal.fullTextAvailability	false
heal.bookName	Encyclopedia of Information Science and Technology, Sixth Edition	en
dc.identifier.doi	10.4018/978-1-6684-7366-5.ch070	el