## 1 Introduction
### Related Work  
Machine learning and game-theoretic approaches continue to find novel applications in performance optimization of vehicular communication networks. The recent advances in UAV-assisted 6G communications that are driving the developments in ITSs have been discussed in [28] and UAV-assisted satellite-aerial-ground integrated networks have been analyzed in [29]. Based on Stackelberg game theory, a novel approach for coordinated UAV-assisted vehicular communication networks was proposed in [30]. The performance of vehicular communications was enhanced using multiple candidates for single-subframe resources in [31]. 1.1 Personalized Federated Learning Approaches and Their Applications in UAV-Assisted Communications  
A variant of FL where the vehicles train personalized local models and make predictions jointly with the UAV's global model is known as PFL. The complexity of the central shared model can be minimized while maintaining the benefits of joint training [32]. In recent applications of PFL, a cloud-based indoor positioning model aggregates parameters from multiple environments to build a cloud-based encoder to create personalized models based on channel state information (CSI). The authors of [33] proposed weight anonymized factorization-based FL for performance improvement and fairness [33]. In addition, a clustered FL algorithm maintains multiple global models to explore latent relationships between clients. The model losses and learning errors are minimized to achieve the optimal number of clusters [34]. Compared to FedAvg and FedSGD, the federated Gaussian process regression (FGPR) framework improves personalization [35]. Dynamic reward shaping improves the selection of actions as state parameters dynamically change, while maintaining reward balance [36]. The authors of [37] proposed an approach that captures common priors from distributed data and generates customized models for heterogeneous clients. Here, a layer-wise PFL algorithm generates and learns different parameters. In a recent work, PFL and DRL maximize the network throughput that adapts to time-varying network states [38]. The authors of [39] proposed a communication-efficient FL through gradient compression and adaptive aggregation. The proposed approach reduced communication overhead between clients and the server [39]. However, a large number of hyper-parameters leads to congestion. To improve communication efficiency, a dynamic sampling and selective masking was used to select fewer clients dynamically in [40]. For efficient wireless FL using layers for personalization, the proposed approach reduced congestion and derived an upper bound for non-convex loss functions. The method also revealed that dynamic network topologies in FL help new participants to reach higher accuracy [41]. The authors of [42] proposed a communication-efficient PFL algorithm and introduced a personalization parameter. The proposed approach improved the model's accuracy and accelerated model convergence by varying the personalization parameter [42]. The authorsof [43] proposed a hierarchical attention-enhanced meta-learning approach that analyzes similarities in clients' data. The method achieves a tradeoff between the clients' personalized models and a common model [43]. In a recent approach, collaborative edge caching was investigated, where fog access points make caching decisions when communicating with a macro base station (MBS) [44]. The authors of [45] applied variational inequality, an incentive-based FL approach, and the block coordinate descent algorithm to edge-based FL [45]. The authors of [46] explored mechanisms to avoid duplicate computations and adapt to data distribution shifts. The authors of [17] considered non-convex Gaussian mixture model (GMM) optimization in edge computing environments and proposed adaptive solutions to the GMM problem. By using an attention mechanism, the priority of services was determined, and resource allocation mechanisms were developed to maintain QoS.