Exploiting UAV for Air–Ground Integrated Federated Learning: A Joint UAV Location and Resource Optimization Approach

Abstract

Recently, many exciting usage scenarios and groundbreaking technologies for sixth generation (6G) networks have drawn more and more attention. The revolution of 6G mainly lies in ubiquitous intelligence, which promotes the development of edge intelligence (EI) by running artificial intelligence (AI) algorithms at the network edge. By embedding training capabilities across the network nodes, federated learning (FL) can achieve high security and alleviate network traffic congestion, which provides a promising way to realize the ubiquitous EI. While traditional FL usually relies static terrestrial base stations (BSs) for the global model aggregation, unmanned aerial vehicles (UAVs) could effectively supplement the terrestrial BSs because of their high maneuverability, thereby building the air-ground integrated FL (AGIFL). Nevertheless, how to effectively deploy the UAV and allocate resources to boost the learning performance and achieve high energy efficiency in the AGIFL remains largely unexplored. In this paper, we study how to jointly optimize the UAV location and resource allocation to minimize the incurred cost in terms of two objectives: i) the minimization of terrestrial users’ energy consumption; ii) the minimization of tradeoff between energy consumption and training latency. The formulated non-convex problems are efficiently solved by alternating optimization techniques based on successive convex approximation (SCA) approaches after appropriate problem decomposition. Extensive simulation results show that our proposed algorithms can reduce more cost than three benchmarks while guaranteeing the learning accuracy. Furthermore, we construct a real-world AGIFL system, implement the proposed algorithms in the system, and carry out field experiments to verify the superiority of our algorithms.