Constrained Federated Learning for AoI-Limited SFC in UAV-Aided MEC for Smart Agriculture

For a wide range of smart agriculture use cases, the prospects of utilizing the Internet of Things (IoT) are immense. Many IoT devices can be deployed for precision farming, soil management, automated irritation, information gathering, or performing some local processing to provide various services. Due to the computational capacity limitation of IoT devices and their limited power, UAV-aided mobile-edge computing (MEC) is proposed to provide IoT nodes with additional resources by hosting their computation functions and making smart agriculture use cases more operational. On the other hand, from the implementation viewpoint, Network Function Virtualization (NFV) is an emerging approach recently proposed for flexible management of such computation functions in UAVs and MEC-server. However, efficient orchestration of the virtualized functions is a challenge. In this paper, we consider a decentralized UAV-aided MEC system in which the NFV-enabled processing nodes manage the computational tasks. To be more specific, we consider the smart agriculture use cases that need live streaming/analysis, such as surveillance or environmental monitoring. In such a network, we propose a method for orchestrating the NFVs efficiently, while the network energy consumption throughout the network is minimized. This problem becomes even more crucial when considering a strict condition on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">instantaneous</i> AoI values. Therefore, the problem is first formulated as a Decentralized <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Constrained</i> Multi-agent Markov Decision Process (Dec-CMMDP). As the formulated problem is NEXP, in the next step, we exploit some structural features of the considered network and introduce the concept of symmetry to simplify the problem. Then, inspired by Augmented Lagrangian dual optimization, a novel decentralized, federated learning-based solution is proposed to solve the problem. Simulation results show the effectiveness of the proposed approach in minimizing the total network energy consumption, minimizing the average AoI, and satisfying the strict condition of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AoI</i> < 100 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">msec</i> for supporting real-time applications in our network parameter settings.