## 2 Related Work  
Although some studies highlight resource-aware FL, very few have explored FL-enabled resource profiling that aids in node selection for optimizing the overall performance of the federated learning context. The following subsections review existing works that considered the resource characteristics of FL, FL-inspired resource profiling, and FL-driven client selection. * **Resource-aware Federated Learning**  
Recently, several research works related to FL have focused on developing algorithms for resource optimization based on one or more criteria, including cost, time, energy consumption, CPU power, and memory, using different algorithms. In [11], the authors developed a deep reinforcement learning algorithm using an optimization function for minimizing the cost based on training time and the consumption of energy. The objective was to increase the speed of training while conserving the energy consumption of mobile devices by controlling the CPU cycle frequency. Furthermore, the authors of [12] proposed an optimization framework based on reinforcement Q-Learning. Their goal was to minimize the communication rounds and maximize the accuracy. A hierarchical game framework was proposed to allow the dynamic edge association and resource allocation in self-organizing Hierarchical FL networks [13]. The authors of [14] introduced the q-Fair optimization objective to emphasize a fair distribution among edges in FL according to performance which is based on the model's accuracy. To minimize the latency of FL training, the authors of [15] proposed a Multi-Armed Bandit algorithm based on training latency, availability, and fairness constraints. A notable contribution in resource allocation and optimization in dynamically evolving network environments is proposed by [PERSON] et al. [16]. Their research investigates the complexities of resource allocation in FL systems assisted by intelligent 104 reflecting surfaces (IRS), particularly under the constraints of imperfect channel state information (CSI). By employing advanced optimization techniques like semi-definite relaxation (SDR) and the constrained concave convex procedure (CCCP), they present a method to significantly reduce training time in these systems ultimately reducing the resource allocation challenges. A framework proposed in [17] describes a resource and data-aware FL approach that characterized the agents/resources according to certain criteria. Initially, it uses the local data size to classify the resources. Other criteria can also be used for resource selection to more accurately classify the resources used in a priority-based decision-making model such as Multiple-Criteria Decision Making Model (MCDM). Experiments showed improvements in the accuracy with less training time over the standard FL baseline. The authors of [18] studied the effects of resource and dataset heterogeneity on the training time of FL deployed on AWS EC2. Their experimentation implied a severe impact of resource heterogeneity, in terms of response time and data quality, and the training time of FL. * **Federated Learning inspired resource profiling**  
Few resource-aware FL frameworks have adopted resource profiling to improve the performance of the FL model. For example, EIFISH is a resource-aware FL framework that adopted resource profiling according to the edge computation capabilities to attain synchronized collaboration [19]. Experimentation showed an improved training accuracy of up to 4% and double the speed over conventional FL schemes. The authors of [20] proposed a Tier-based FL system that classified the clients adaptively into groups according to their performance, then selected only the nodes within the same group to avoid straggler problems and delays caused by the slowest devices resulting from resource heterogeneity. The results from their experiments showed that the proposed approach improved the training speed while keeping the accuracy the same or slightly increased.