TactiFlex: A Federated learning-enhanced in-content aware resource allocation flexible architecture for Tactile IoT in 6G networks

The Tactile Internet of Things (TIoT) is transforming the landscape of real-time applications by enabling haptic interactions and immersive experiences. This paper explores the potential of TIoT applications in critical sectors such as healthcare and manufacturing, emphasizing the necessity of ultra-reliable, low-latency communication. Conventional network infrastructures fall short of meeting these demands, necessitating innovative solutions such as Network Slicing (NS) to customize the network according to user activities. One of the key challenges addressed in this research is the allocation of resources for tactile data, which requires specialized solutions to prevent performance degradation in shared environments. Additionally, the paper proposes a solution that includes in-content awareness, enabling precise resource allocation based on the user’s intent and requirements. Dynamic resource scaling, proactive resource allocation, and optimized bandwidth usage are essential components of the proposed architecture, guaranteeing responsive and efficient user experiences. Furthermore, the research introduces an end-to-end network slicing (NS) solution, emphasizing the importance of considering all components of the TIoT chain to prevent bottlenecks. Machine learning plays a crucial role in translating TIoT service profiles into specific requirements that are in line with the evolving needs of TIoT. To overcome the limitations of deep learning (DL), federated learning (FL) emerges as a groundbreaking approach, enabling collaborative model training without compromising data privacy. The paper explores the potential of FL and addresses its limitations within a centralized framework. It advocates for a novel architecture that integrates blockchain technology, Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Multi-Access Edge Computing (MEC) to enhance FL in TIoT applications. The study investigates the performance of lightweight deep learning methods used as local models in federated learning for TIoT applications. The research also analyzes various FL algorithms from different perspectives, considering various local models contributing to the global model. Additionally, the study evaluates how the selected FL algorithms and DL local models collaborate, providing valuable insights into the performance and efficiency of the proposed architecture. These advancements aim to revolutionize the applications of TIoT and usher in a new era