Energy-Efficient Offloading, Caching, and Resource Allocation for Blockchain-Assisted Low-Altitude Flying Networks: An Integrated Federated Learning and MAPPO Approach

Abstract

In 6G low-altitude edge intelligent networks, the proliferation of delay-sensitive Internet of Things (IoT) services exacerbates mutual interference among IoT devices, thereby reducing the efficiency of resource allocation. To address this challenge, we propose a blockchain-assisted federated learning (FL)-based four-layer low-altitude framework, where the user equipment (UE) layer generates delay-sensitive tasks, the task UAV (TUAV) layer offers extra computation resources to process UE tasks, the service UAV (SUAV) layer trains the FL local models in a distributed manner, and the base station layer aggregates them to obtain the global model, where the FL models are trained for the joint optimisation including resource allocation, offloading decisions, and caching strategy. Based on this framework, we define a system utility cost that incorporates the total service delay (including the transmission, computation, queueing, and fetching delays) and the total energy consumption, and formulate an optimisation problem to minimise the long-term system utility cost. To solve the formulated problem, we propose an FL and multi-agent proximal policy optimisation (MAPPO) integrated offloading decisions, caching strategy, and resource allocation. Simulation results show that our proposed algorithm converges faster and achieves a lower system utility cost than the benchmarks.