Investigating Federated Learning Implementation Challenges in 6G Network
- Resource Type
- Conference
- Authors
- Paul, Suman
- Source
- 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 2024 Fourth International Conference on. :1-6 Jan, 2024
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
6G mobile communication
Privacy
Federated learning
5G mobile communication
Market research
Edge intelligence
federated learning
key performance indictors
machine learning
quality of service
6G network
- Language
The rapid progress and deployment beyond 5G and moving towards a 6G network demand the stipulation for network intelligence by applying advanced ML-driven approaches. The modern state-of-the-art ML-driven approaches in existing 5G network have various concerns of communication and computing resource scarcity, computing overhead and privacy issues. Federated learning (FL) incorporated 6G network will play a significant role in overcoming such challenges. However, the FL-incorporated 6G network suffers due to various implementation challenges considering 6G and FL. In this paper, the author discusses the potential of FL for the requirements of 6G and investigates various federated learning implementation challenges, followed by trends and developments of FL-incorporated massive edge intelligence (EI) in future 6G in a comprehensive way.