menu
{ "item_title" : "Collaborative Learning for 6g Mobile Wireless Networks", "item_author" : [" Houda Hafi", "Bouziane Brik", "Zakaria Abou El Houda "], "item_description" : "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.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/0/44/340/570/0443405700_b.jpg", "price_data" : { "retail_price" : "165.00", "online_price" : "165.00", "our_price" : "165.00", "club_price" : "165.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Collaborative Learning for 6g Mobile Wireless Networks|Houda Hafi

Collaborative Learning for 6g Mobile Wireless Networks

PRE-ORDER NOW:
local_shippingShip to Me
Preorder. This item will be available on June 1, 2026 .
FREE Shipping for Club Members help

Overview

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.

This item is Non-Returnable

Details

  • ISBN-13: 9780443405709
  • ISBN-10: 0443405700
  • Publisher: Academic Press
  • Publish Date: June 2026
  • Shipping Weight: 0.99 pounds
  • Page Count: 284

Related Categories

You May Also Like...

    1

BAM Customer Reviews