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

Qamar F, Kazmi SHA, Siddiqui MUA, Hassan R, Zainol Ariffin KA.

Open source

DOI: 10.7717/peerj-cs.2360
Published: 2024-10-09
Container: PeerJ Comput Sci
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.7717/peerj-cs.2360,
  title = {Federated learning for millimeter-wave spectrum in 6G networks: applications, challenges, way forward and open research issues.},
  author = {Qamar F and  Kazmi SHA and  Siddiqui MUA and  Hassan R and  Zainol Ariffin KA.},
  year = {2024},
  journal = {PeerJ Comput Sci},
  doi = {10.7717/peerj-cs.2360},
  url = {https://doi.org/10.7717/peerj-cs.2360}
}

RIS

TY  - JOUR
TI  - Federated learning for millimeter-wave spectrum in 6G networks: applications, challenges, way forward and open research issues.
AU  - Qamar F
AU  -  Kazmi SHA
AU  -  Siddiqui MUA
AU  -  Hassan R
AU  -  Zainol Ariffin KA.
PY  - 2024
JO  - PeerJ Comput Sci
DO  - 10.7717/peerj-cs.2360
UR  - https://doi.org/10.7717/peerj-cs.2360
ER  -

APA

F, Q., SHA, K., MUA, S., R, H., & KA., Z. A. (2024). Federated learning for millimeter-wave spectrum in 6G networks: applications, challenges, way forward and open research issues.. PeerJ Comput Sci. https://doi.org/10.7717/peerj-cs.2360

Source records

europe-pmc · retrieved 2026-09-25T15:25:47.006Z
doaj · retrieved 2026-09-25T15:25:46.977Z