Edge-Native Intelligence for 6G Communications Driven by Federated
Learning: A Survey of Trends and Challenges

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

14 November 2021

Mohammad M. Al-Quraan

Lina S. Mohjazi

Mérouane Debbah

Muhammad Ali Imran

Papers citing "Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges"

9 / 9 papers shown

Title
Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights Maryam Ben Driss Essaid Sabir H. Elbiaze Walid Saad 23 7 0 07 Dec 2023
Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications Azim Akhtarshenas Mohammad Ali Vahedifar Navid Ayoobi B. Maham Tohid Alizadeh Sina Ebrahimi David López-Pérez FedML 23 4 0 08 Oct 2023
Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition Mohammad M. Al-Quraan A. Zoha Anthony Centeno H. Salameh Sami Muhaidat Muhammad Ali Imran Lina S. Mohjazi 8 5 0 22 Jun 2023
Towards Personalized Federated Learning A. Tan Han Yu Li-zhen Cui Qiang Yang FedML AI4CE 183 840 0 01 Mar 2021
Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO Ahmet M. Elbir Sinem Coleri 24 128 0 25 Aug 2020
Survey of Personalization Techniques for Federated Learning V. Kulkarni Milind Kulkarni Aniruddha Pant FedML 168 323 0 19 Mar 2020
Threats to Federated Learning: A Survey Lingjuan Lyu Han Yu Qiang Yang FedML 186 432 0 04 Mar 2020
All Reality: Virtual, Augmented, Mixed (X), Mediated (X,Y), and Multimediated Reality Steve Mann Tom A. Furness Yu Yuan Jay Iorio Zixin Wang 16 138 0 20 Apr 2018
Adaptive Federated Learning in Resource Constrained Edge Computing Systems Shiqiang Wang Tiffany Tuor Theodoros Salonidis K. Leung C. Makaya T. He Kevin S. Chan 141 1,680 0 14 Apr 2018