Cargando…
Federated learning for 6G-enabled secure communication systems: a comprehensive survey
Machine learning (ML) and Deep learning (DL) models are popular in many areas, from business, medicine, industries, healthcare, transportation, smart cities, and many more. However, the conventional centralized training techniques may not apply to upcoming distributed applications, which require hig...
Autores principales: | Sirohi, Deepika, Kumar, Neeraj, Rana, Prashant Singh, Tanwar, Sudeep, Iqbal, Rahat, Hijjii, Mohammad |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10008151/ https://www.ncbi.nlm.nih.gov/pubmed/37362891 http://dx.doi.org/10.1007/s10462-023-10417-3 |
Ejemplares similares
-
Blockchain for COVID-19: a comprehensive review
por: Shah, Het, et al.
Publicado: (2021) -
Enabling Secure XMPP Communications in Federated IoT Clouds Through XEP 0027 and SAML/SASL SSO
por: Celesti, Antonio, et al.
Publicado: (2017) -
AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure
por: Rathod, Tejal, et al.
Publicado: (2023) -
Federal information security
por: Miller, David G, et al.
Publicado: (2012) -
Challenges and future directions of secure federated learning: a survey
por: Zhang, Kaiyue, et al.
Publicado: (2021)