As a Ph.D. student at Chalmers University, my research focuses on advancing federated learning, specifically exploring the privacy implications within this paradigm. In this project, we want to design a subgraph federated learning model that avoids exchanging raw node features and yet gains a high accuracy rate compared to the case that it is possible to exchange raw node features. This project has a lot of applications in various field such as anti-money laundering, Health-care, ... The project's core objective is to apply Graph Neural Network (GNN) architectures in federated learning, addressing the challenges posed by privacy concerns. Given the substantial size of real-world graphs, I require robust computational resources to conduct experiments and generate meaningful results. The immediate goal is to publish a paper in the machine learning domain, requiring the generation of results through systematic experimentation. The utilization of Chalmers server resources is fundamental to achieving the technical success necessary for the publication of this paper. Chalmers server resources are instrumental for the technical success of my research on subgraph federated learning. The synergy between Python, VS Code, machine learning packages, and Chalmers servers empowers efficient experimentation and facilitates the derivation of valuable insights.