Emerging technologies and applications, including the Internet of Things (IoT), social networking, and crowd-sourcing, generate large amounts of data at the network edge. Artificial Intelligence-based models are often built from the collected data to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all of the data to a centralized location. Federated learning has been used to overcome the desired needs in wireless communication.

Across the world, the deployment of 5G communication networks has achieved a big milestone in academia and industry. A remarkable achievement has been made in wireless communication, and now looks to move beyond 5G. The exploration of 6G has been emerging in the artificial intelligence (AI) platform to collect sensor data with the support of high-performance computing networks. The traditional process involves a collection of the data in a centralised manner and henceforth the heterogenous data from the various sources is accumulated in the server, leading to central issues in communication. AI-based Federated Learning (FL) is a bottleneck technology that enhances the privacy and security issues in the wireless paradigm. Federated Learning is a distributed platform of AI-based approaches that enhances the smart system's connectivity with increased network capacity, quality of service, network availability, and user-experience. Advanced mathematical tools in the field of wireless communications with FL helps the process in telecom, bioinformatics, healthcare, Internet of Things, social networks, and manufacturing. This emerging FL-based 6G communication improves the automation and the optimised transmission for next generation data networks.

The main focus of this Special Issue is the most recent applications of Federated Learning in 6G to optimize data for next-generation networks. The aim of this Special Issue is to disseminate the latest research and innovations in this field. Various new 6G technologies of interest include: man-machine interactions with various collections of data from multiple devices; cloud-based distributed connections between multiple devices; new mixed multi-sensor data collection and data security; and precision sensing that maps the wireless communication with the physical world. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Mobile network-based secure spectrum allocation using AI-based FL
• Blockchain and IoT applications with mobile networks using AI-based FL
• Game theory applications for mobile networks using AI-based FL
• 6G secure wireless communication using AI-based FL
• Mobile network-based wireless modulation and coding using AI
• Approaches for millimetre wave technologies using FL
• Approaches for ultra-dense cell communication using AI-based FL
• Approaches for physical layer heterogeneous networks using AI-based FL
• AI approaches for unmanned aerial vehicles (UAVs) techniques using FL
• Applications of FL-based AI approaches for wireless communications technologies
• Edge/IoT-based mobile networks using FL