Applications, Challenges, and Opportunities for Federated Learning in 6G
Roheen Qamar, Saima Siraj · 2024
Following the global deployment of 5G communication networks, business, and academics are looking at 6G as a successor. Most think that ubiquitous artificial intelligence (AI) will enable data-driven machine learning solutions in enormously heterogeneous networks, setting the framework for 6G. However, standard machine learning algorithms require centralized data collection and processing by a single server, which is impractical for large-scale daily applications owing to privacy issues. Federated learning (FL) is a novel form of distributed AI that prioritizes privacy. It is appealing for a wide range of wireless applications, particularly since it is required for ubiquitous AI in 6G. The standard machine learning approach entails centralizing training data in a data center for analysis and inference using centralized machine learning algorithms. Wireless networks’ limited communication capacity and privacy concerns make it difficult for devices to send data to parameter servers. FL, which allows devices to train a shared machine learning model without requiring data exchange or transmission, may address these issues. This article provides detailed coverage of FL applications for 6G wireless networks. The first section focuses on the FL requirements for wireless communication. Traditional machine learning occurs in data centers or clouds. Security concerns and the abundance of data and processing resources available on wireless networks are driving the implementation of learning algorithms closer to the network edge. We established FL, a rapidly expanding field that combines machine learning with wireless communication. This chapter investigates the use of FL in 6G wireless networks as well as its drawbacks.