Federated learning-empowered mobile network management for 5G and beyond networks: From access to core

J Lee, F Solat, TY Kim, HV Poor - … Communications Surveys & …, 2024 - ieeexplore.ieee.org
The fifth generation (5G) and beyond wireless networks are envisioned to provide an
integrated communication and computing platform that will enable multipurpose and …

A survey on explainable ai for 6g o-ran: Architecture, use cases, challenges and research directions

B Brik, H Chergui, L Zanzi, F Devoti, A Ksentini… - arxiv preprint arxiv …, 2023 - arxiv.org
The recent O-RAN specifications promote the evolution of RAN architecture by function
disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop …

A survey of trustworthy federated learning: Issues, solutions, and challenges

Y Zhang, D Zeng, J Luo, X Fu, G Chen, Z Xu… - ACM Transactions on …, 2024 - dl.acm.org
Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …

Trustworthy federated learning: A comprehensive review, architecture, key challenges, and future research prospects

A Tariq, MA Serhani, FM Sallabi… - IEEE Open Journal …, 2024 - ieeexplore.ieee.org
Federated Learning (FL) emerged as a significant advancement in the field of Artificial
Intelligence (AI), enabling collaborative model training across distributed devices while …

Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing

S Roy, H Chergui, C Verikoukis - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Future zero-touch artificial intelligence (AI)-driven 6G network automation requires building
trust in the AI black boxes via explainable artificial intelligence (XAI), where it is expected …