## 4 Resource-Aware Federated Profiling Model  
In this section, an overview of our resource-aware federated profiling model, the formulation of the edge workload profiling and an explanatory example of the edge workload profile is provided. In order to make the most effective use of the distributed computing resources at the edges, the resource-aware federated profiling framework was established. 1. **Model Overview** The Federated Workload Profiling model proposed in this section addresses resource planning. Planning of the edge workload is formulated as a resource optimization problem that eliminates the nodes that do not meet the computing demands. The model as shown in Figure 2, is a hybrid resource profiling model that combines both static and dynamic profiling. Static profiling mainly depends on the physical characteristics of the edge resource's, such as the CPU's computation power, memory size, and transmission power. On the other hand, dynamic profiling relies on the collected real-time available resources and the performance metrics at the edge. The real-time available resources include but are not limited to CPU utilization, memory usage, and network delays. However, the edge's performance metrics are based on data training performance, such as accuracy and convergence time of the model. Figure 2: The Federated Workload Profiling model. Initially, a baseline resource profile is created based on the static profiling and is collected from all edges. An Edge Workload Profile (EWP) is created based on the configuration file available at the edge and a real-time measurement of the parameters including the task, the incoming data, the outgoing data, the infrastructure (CPU/GPU, storage), the mobility of the edge device, the round-trip latency based on the current location and the reputation of the edge. The Edge Workload Profiling Module (EWPM), which is responsible for continuously updating the EWP, receives information about the dynamic characteristics of the edge resources from the Node Resource Monitoring Agent (NRMA) at the edge node. Each client creates their own edge workload profile, which is then sent to the federation server, where the Federated Resource Profile is built, in accordance with the federation's rules. The EWP's resource parameters and the edge node's reputation that sustain prior edge performance, both are taken into consideration by the federation's rules. The Federated Workload Profiling (FWP) module aggregates EWP's from all nodes and provides inputs to the Node Selection Module (NSM) that selects the edges depending on the thresholds of the resource parameters that have been stated in advance.The thresholds for CPU, memory, network, sensors, and storage metrics for the NSM depends on the system's requirements and resources, but a general guideline is provided in Table 2. It is important to note that these thresholds are not set rigidly and may need to be adjusted based on the specific requirements of the system and the workload being processed. The effect of different thresholds may vary depending on the number of edges and their individual resource capabilities. For example, if the threshold for network usage is set too low, it may lead to slower communication and longer training times, particularly if there are a large number of edges. On the other hand, if the threshold is set too high, it may lead to network congestion and communication failures. Additionally, the thresholds can be set dynamically based on the workload and resource utilization patterns over time. Resource profiling will initially rely on expert-set thresholds, using their deep understanding of system capabilities and expected workloads. Over time, as the system accumulates and analyzes historical data on resource usage, these thresholds can be refined to more accurately reflect the real-time demands and efficiency requirements. This evolving process allows the system to dynamically adapt its resource allocation, enhancing performance and resource management based on actual usage patterns.