## 1 Introduction  
Healthcare applications and devices collect vast amounts of patient data from sources such as sensors and medical equipment. These data have grown exponentially in recent years, making it difficult for providers to efficiently manage the data and associated resources. Edge computing is essential for utilizing automation and machine learning technologies to process and store massive amounts of data at the network's edge, close to the point of collection. This will allow doctors to take immediate actions to help patients avert medical emergencies. The healthcare sector may become more streamlined and efficient, thanks to edge computing. The concept of Federated Learning (FL) [1; 2; 3] has recently gained popularity in the healthcare industry as a means of solving the issues of data privacy and security through the cooperative training of algorithms that does not require the sharing of individual data sets, while it can be challenging to obtain large amounts of medical data, FL helps solve this problem by facilitating learning through collaboration rather than data centralization. Without transferring patient data outside of their current institutions' firewalls, FL enables interactive insight-gathering, for example in the form of a consensus model. Instead, ML is performed locally at each participating institution, and only the characteristics of the models themselves (such as parameters and gradients) are shared. Researchers have found that FL-trained models outperform those that have access to only data from a single institution or node. When the data defining the optimizer is dispersed unevenly among a very large number of nodes, ML presents another important context for distributed optimization. The goal was to minimize the number of communication rounds while maintaining highly efficient communication in order to train a high-quality centralized model known as Federated Optimization [4], which can provide on-device intelligence. With the increasing edge computing challenges of processing, storage, and network I/O, federated resource profiling is a promising approach that can assess local computation capacities and enhance the resource utilization capabilities of a distributed computing environment. FL supported by resource profiling will have a minimal overhead because it will only evaluate the measurements and variance of the edge node's resources. Quality of Service (QoS) and Quality of Experience (QoE) are key metrics in evaluating telecommunication services. QoS primarily concerns the technical aspects and performance metrics of a service, such as latency and bandwidth, while QoE focuses on the subjective user experience, encompassing aspects like satisfaction and usability. Together, these metrics provide a comprehensive understanding of service effectiveness from both technical and user-centric perspectives. Given that health tasks require a significant amount of computing power, the system must ensure strict QoS and QoE requirements. Moreover, each edge node can only perform computations up to its maximum capacity. A data-driven federation offers a trifecta of benefits, including the ability to optimize the consumption of data resources, reduce transportation costs, and enforce data privacy. The proposed Federated Resource Profiling (FRP) model is intended to be used in the context of Patient Similarity Networks (PSNs) [5; 6; 7; 8] and Federated PSN (FPSNs) [9], a relatively new network-based approach in precision medicine in which patients are grouped or categorized according to their similarities in a variety of characteristics. Similar instances may be grouped together to reveal common patterns in the progression of illnesses [10]. FPSN models distribute processing across federated nodes and then fuse the PSN matrices in the cloud, highlighting the need for a balance between computational speed and accuracy.