## 1 Introduction  
Unmanned aerial vehicles (UAVs) find diverse applications in vehicular communications in sixth-generation (6G) networks. UAVs can serve as aerial computing equipment or aerial base stations (BSs) to provide a wide range of services to vehicular networks [1]. Moreover, UAVs have a great number of degrees of freedom in their trajectory and can achieve line of sight (LoS) with vehicles [2]. However, optimizing the available UAV battery power during massive data transfers and multi-link communications originating from multiple vehicles is a critical issue [3]. The operational height and elevation angle of a UAV also impact its link reliability and coverage range. The optimal trajectory of UAVs is subject to interference constraints while maximizing the coverage area. The mobility of vehicles and the UAV and the associated Doppler spread further impact the precision in channel estimation methods [4]. In 6G communications, the volume of data traffic has surged as sensors generate, offload, and process huge amounts of data [5]. These applications require substantial computing resources and need energy-efficient mechanisms to store and process the incoming packets at the UAV's server. A combination of federated learning (FL) and mobile edge computing (MEC) facilitates collaborative intelligence in vehicular edge computing [6]. Mobile cloud computing (MCC) and MEC have been used as viable solutions to process computationally intensive tasks at vehicular edge servers [7]. In some approaches, Lyapunov optimization has been used to reduce latency, enhance energy efficiency, and achieve convergence of underlying machine learning models. When MEC is combined with FL, partial offloading improves the quality of service (QoS) in low-latency and delay-sensitive vehicular communications [8]. Edge intelligence for FL-driven 6G communications was discussed in [9], while emerging wireless networks equipped with machine learning were discussed in [10]. An example scenario with integration of a UAV in vehicular communications is depicted in Figure 1. Here, a conventional road side unit (RSU) communicates with vehicles. To facilitate this communication, the UAV can provide communication range for all vehicles [11]. With the UAV serving as a mobile base station (BS), the handoff delay can be reduced to minimal. Figure 1 illustrates that with a traditional terrestrial BS, the signal strength is low when the vehicles are distant from the BS. Figure 1 also illustrates that the handoff delay accumulation can be reduced even in the presence of some no-coverage zones by deploying a UAV as a mobile BS. However, a UAV has limited battery power and needs to remain in a vehicle's LoS for a prolonged time. The paradigm of FL addresses challenges such as limited scalability of centralized computing server, centralized storage and management of vehicular data, computational complexity at the vehicular edge, and inconsistencies due to the straggler effect [12]. Excluding stragglers reduces the amount of training data, leading to sparse gradients and model aggregation errors [13]. Furthermore, federated stochastic gradient descent (FedSGD) and federated averaging (FedAvg) limit the scalability of FL approaches [14]. Some recent applications of distributed FL approaches for intelligent transportation systems (ITSs) have been discussed in [15]. Furthermore, the transmission of messages from multiple vehicles leads to wastage of communication resources when the messages at successive transmission time intervals (TTIs) have similar content [16]. The relevance of a global model depends on data distributions among vehicles. Specifically, a global model works well when vehicles' data is independent and identically distributed (i.i.d.). Hence, this paper explores the application of personalized federated learning (PFL) in UAV-assisted C-V2X communications to optimize the available UAV transmit power and addresses resource demands of vehicles [17].