H-HOME: A learning framework of federated FANETs to provide edge computing to future delay-constrained IoT systems

In 6G systems, it will be mandatory that the network is able to support edge computing powered by Artificial Intelligence (AI) to provide mobile devices with the opportunity of job offloading for computation, so implementing the new paradigm of Intelligent Internet of Intelligent Things (IIoIT). In areas that are very difficult to be covered by the structured networks, Flying Ad-Hoc Networks (FANET) can be considered one of the most promising technologies to enhance coverage, capacity, reliability, and energy efficiency of wireless cellular networks, also providing edge-computing services. The goal of this paper is to propose a two-layer Hierarchical Horizontal-Offload ManagEment (H-HOME) framework for horizontal offload among the Unmanned Aerial Vehicles (UAV) of the same FANET, in order to minimize processing delay and jitter. The framework exploits Federated Reinforcement Learning in order to take advantage of knowledge sharing without incurring into problems of privacy and network overloading. A Markov Decision Process (MDP) is also defined to optimize decisions of the FANET Orchestrator (FO). Simulation results, obtained from two different analyses, demonstrate that H-HOME outperforms the traditional local training approach, based on simple recursive learning, and it can be effectively used to reduce power consumption and increase FANETs flight autonomy.