Graph machine learning has recently gained significant traction in academia and industry. Machine learning models, such as graph neural networks (GNNs), are generally trained on massive graph datasets. In most real-world settings, however, such as banking transaction networks and healthcare networks, this data is localized with multiple data owners and cannot be aggregated due to privacy concerns. Federated Graph Learning (FGL) has emerged as a viable approach to tackle this issue. FGL involves training shared graph machine learning models locally at the data owners and aggregating them to achieve a global GNN model, while simultaneously addressing privacy concerns and regulations. While FGL has seen focused research interest in recent years, with several new frameworks proposed and existing federated learning (FL) frameworks adding support for FGL, few works have attempted to adapt the standard FL components, such as client selection and aggregation for the specific application of FGL. We present a preliminary study on adapting the standard aggregation algorithms used in FL for FGL by leveraging graph topology. This study is part of a broader objective to explore solutions for a resource-efficient, robust, and scalable FGL framework.