## 2 Related Work
Furthermore, the authors of [21] adopted a low-complexity profile-based resource-aware allocation strategy in FL called Dispersed Federated Learning (DFL). They implemented an optimization problem for resource allocation and device association by defining the preference and resource profiles. Preference profiles were used to allocate resource blocks to devices having a lower cost. Alternatively, the authors of [22] proposed a FL time minimization problem to control the data size used in each device using the makespan minimization problem and assigned identical tasks to heterogeneous resources. They adopted an optimal polynomial-time scheduling algorithm called OLAR. 3. **Federated Learning-driven edge node selection** In FL, the number of clients may be large, but the bandwidth available for model distribution and re-uploading is limited, making it advisable to only involve only a few of the edge nodes in the training process at any given time. This is a challenge that must be overcome to ensure that the model training process runs smoothly and without delay. With regards to the training efficiency, the quality of the final model, and its fairness, a client selection policy is crucial to a FL process. In [23], the authors proposed a model wherein the Lyapunov optimization problem of selecting clients with the highest degree of fairness was handled. Although FL has the potential to safeguard privacy in distributed machine learning, its limited applicability is due to inefficiencies in its communication [24]. With hierarchical FL (HFL), workers who own the data first send their updated model parameters to the edge servers for intermediate aggregation before sending everything to the central server for global aggregation. For the effective HFL deployment, it is necessary to address the issues of edge association and resource allocation for non-cooperative parties such as edge servers and model owners. Using the edge server's predicted bandwidth allocation management approach in self-organizing HFL networks, a hierarchical game framework [13] is proposed to analyze the dynamics of edge association and resource allocation, while collecting more local data from the clients enables more precise global model building; the resulting heavy data arrivals at the edge could be counterproductive to queue stability. The authors of [25] proposed an algorithm that varied the number of clients chosen based on their current resource usage for transmission in order to maximize the time-averaged FL accuracy while maintaining the queue's stability. In a traditional wireless network, an FL setup involves clients sharing a wireless connection to a central server where the federated models are trained. Due to the fact that both radio energy and the client's energy are limited [26], the learning performance is dependent on how the clients are selected and how the bandwidth is distributed among the selected clients in each learning round. To formulate a stochastic optimization problem for joint client selection and bandwidth allocation under long-term client energy constraints, the article [27] developed a new algorithm that used only the currently available wireless channel information but still guaranteed long-term performance. 4. **QoS and QoE in Federated Learning Systems** QoS and QoE are fundamental to the effectiveness of FL systems, especially in edge computing environments and is particularly crucial in sectors like healthcare [28], where the precision and speed of data processing are essential. QoS, as defined by ITU-T E.800, encompasses critical service characteristics like latency and bandwidth that directly impact the performance of FL systems [29]. QoE, on the other hand, focuses on the user's perception and satisfaction with the service, as outlined by ITU-T P.10 and Qualinet [30]. Recent studies emphasize the importance of QoS and QoE in heterogeneous networks. [PERSON] et al. [31] address QoS estimation challenges, proposing a normalization approach for telecommunication systems. [PERSON] et al.