## 1 Introduction
### Related Work
The results demonstrated a decrease in task processing delays, and effectively delivered QoS [14]. 1.2 Personalized Federated Learning Approaches for Performance Enhancement in Aerial-Ground Integrated Networks  
Existing FL schemes do not adequately account for performance complexity when clients conduct a varying number of training iterations [47]. When clients have low computational or communication capabilities, global model aggregation is slow, causing higher latency. The authors of [48] proposed time-sensitive FL and investigated whether FL leads to improvement in the convergence rate. The authors proposed a DRL mechanism to develop an optimal deterministic algorithm for assigning local iterations based on the computational capabilities of clients [48]. In addition, to minimize training time, the authors in [49] implemented a framework that proposes a spatial-temporal learning optimization based on the resources available at the UAV. The solution approach in [49] utilizes dual fitting to create an online algorithm with a competitive ratio and reduces average flow time by 19% [49]. The authors of [50] proposed collaborative neural filtering for communication efficiency. To reduce model complexity, the authors constructed a standard neural collaborative filtering to select smaller payloads. FL models from selected nodes resulted in a payload-efficient global model. The authors of [51] integrate FL with multi-agent DRL in the vehicular environment enhance energy efficiency and task completion rates. The task-offloading mechanism establishes an FL framework to determine resource allocation proportions [51]. Conventionally, communication and information fusion models have been designed based on belief maps, and multi-objective optimization problems have been formulated as Markov decision processes (MDPs) [52]. A novel method examines the criterion for selecting participating devices under energy constraints and their impact on device selection. To improve training efficiency, the method uses the age-of-information (AoI) metric to determine how quickly participants update their gradients [53]. The authors of [54] propose an innovative Stackelberg game-based framework that explores global loss minimization and latency minimization for faster convergence. To reduce communication rounds, global loss minimization is formulated at the leader, while latency minimization is formulated at the followers. Utilizing monotonic optimization and matching theory, they achieve an optimal follower strategy through subproblems pertaining to resource allocation and sub-channel assignment [54]. The authors of [55] obtained an upper bound for the convergence rate and proposed a new algorithm for selecting devices based on the age of update. It was shown that AoI-based device selection accelerates convergence rates and makes efficient use of sub-channels [55]. The authors of [56] investigate an actor-critic framework for heterogeneous asynchronous FRL. A three-layer MEC system involves entities acting both as actors, interacting with the environment, and critics, learning optimal policies, with independent training for each agent. The method solves the problem of non-stationary environments and enablescooperation among entities [57]. To minimize delay, the authors in [58] formulate an MDP. A solution approach based on DDPG and k-nearest neighbors is used [58]. In a novel approach in [59], an actor network generates user scheduling, UAV trajectory, and power allocation policies, updating dual variables. The authors of [60] proposed an air slicing in which multiple UAVs serve as aerial BSs. With network slicing, the network efficiency is maximized while achieving a simultaneous improvement in latency. The authors formulate a stochastic game in MDP environments where multiple adaptive agents compete simultaneously [60].