Federated learning for millimeter-wave spectrum in 6G networks: applications, challenges, way forward and open research issues

被引:4
作者
Qamar, Faizan [1 ]
Kazmi, Syed Hussain Ali [1 ]
Siddiqui, Maraj Uddin Ahmed [2 ]
Hassan, Rosilah [1 ]
Arif, Khairul Akram Zainol [1 ]
机构
[1] Univ Kebangsaan Malaysia, Fac Informat Sci & Technol, Ctr Cyber Secur, Bangi, Selangor, Malaysia
[2] Univ Glasgow, James Watt Sch Engn, Glasgow, Scotland
关键词
mmWave; Federated learning; Beamforming; 6G; MIMO; MASSIVE MIMO; CHANNEL ESTIMATION; RESOURCE-ALLOCATION; MMWAVE BEAM; COMMUNICATION; FUTURE; SYSTEMS; TRANSMISSION; TECHNOLOGIES; SELECTION;
D O I
10.7717/peerj-cs.2360
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The emergence of 6G networks promises ultra-high data rates and unprecedented connectivity. However, the effective utilization of the millimeter-wave (mmWave) as a critical enabler of foreseen potential in 6G, poses significant challenges due to its unique propagation characteristics and security concerns. Deep learning (DL)/machine learning (ML) based approaches emerged as potential solutions; however, DL/ML contains centralization and data privacy issues. Therefore, federated learning (FL), an innovative decentralized DL/ML paradigm, offers a promising avenue to tackle these challenges by enabling collaborative model training across distributed devices while preserving data privacy. After a comprehensive exploration of FL enabled 6G networks, this review identifies the specific applications of mmWave communications in the context of FL enabled 6G networks. Thereby, this article discusses particular challenges faced in the adaption of FL enabled mmWave communication in 6G; including bandwidth consumption, power consumption and synchronization requirements. In view of the identified challenges, this study proposed a way forward called Federated Energy-Aware Dynamic Synchronization with Bandwidth-Optimization (FEADSBO). Moreover, this review highlights pertinent open research issues by synthesizing current advancements and research efforts. Through this review, we provide a roadmap to harness the synergies between FL and mmWave, offering insights to reshape the landscape of 6G networks.
引用
收藏
页数:40
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