Joint UAV Location and Resource Allocation for Air-Ground Integrated Federated Learning

With the envision of sixth generation (6G) network technology, varied artificial intelligence (AI) services gradually develop from the network center to the edge, which makes unmanned aerial vehicle (UAV) a hot spot to provide auxiliary services of machine learning (ML) to empower terrestrial users intelligence. However, due to the sensitive privacy and limited resources, traditional centralized ML may not be used directly in such networks. As a promising distributed collaborative ML, fed-erated learning (FL) could be more suitable. Meanwhile, unlike conventional FL working on terrestrial networks, applying FL in UAV-assisted networks should strictly consider the impact of air-ground wireless channel caused by the maneuverability of UAVs, as well as the allocation of various network resources, including frequency and latency. To address these challenges, we propose to jointly optimize the UAV location and resource allocation, subject to the constraints of learning accuracy and training latency to minimize the energy consumption of terrestrial users. The for-mulated complicated non-convex problem is efficiently solved by an alternating optimization algorithm based on successive convex approximation (SCA) approaches after problem decomposition. Simulations results show that our proposed algorithm can reduce more overall users' energy consumption than three benchmarks while guaranteeing the learning accuracy within the maximum training latency.