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- Leidėjas: Elsevier Science
- Metai: 2026
- Puslapiai: 375
- ISBN-10: 0443405700
- ISBN-13: 9780443405709
- Formatas: x x cm, minkšti viršeliai
- Kalba: Anglų
- Kaina galioja įvedus kodą: ENG
Collaborative Learning for 6g Mobile Wireless Networks (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
Collaborative Learning for 6G Mobile Wireless Networks gives a comprehensive introduction to the topic and its potential role in the development of 6G by explaining principles and presenting methods, algorithms, and uses cases. To achieve 6G’s vision of intelligent and autonomous networks capable of self-optimization, self-healing, and context-aware adaptation, there is a need to develop advanced algorithms and frameworks to enable network elements to perceive, reason, and act autonomously in dynamic and unpredictable environments. However, traditional machine learning methods rely on centralized data collection and processing, making it a limitation for large-scale applications.
Collaborative learning, as an emerging distributed approach, offers a powerful framework for harnessing the collective intelligence of distributed data sources while addressing key challenges such as privacy and security.
Kaina galioja įvedus kodą: ENG
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- Leidėjas: Elsevier Science
- Metai: 2026
- Puslapiai: 375
- ISBN-10: 0443405700
- ISBN-13: 9780443405709
- Formatas: x x cm, minkšti viršeliai
- Kalba: Anglų
Collaborative Learning for 6G Mobile Wireless Networks gives a comprehensive introduction to the topic and its potential role in the development of 6G by explaining principles and presenting methods, algorithms, and uses cases. To achieve 6G’s vision of intelligent and autonomous networks capable of self-optimization, self-healing, and context-aware adaptation, there is a need to develop advanced algorithms and frameworks to enable network elements to perceive, reason, and act autonomously in dynamic and unpredictable environments. However, traditional machine learning methods rely on centralized data collection and processing, making it a limitation for large-scale applications.
Collaborative learning, as an emerging distributed approach, offers a powerful framework for harnessing the collective intelligence of distributed data sources while addressing key challenges such as privacy and security.
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