The rapid development of Machine Learning algorithms and a growing range of its applications, as well as an increasing number of Edge Computing devices, created a need for a new paradigm that would benefit from both fields. Federated Learning, which emerged as an answer to this need, is a technique that also solves privacy-related issues arising when large amounts of information are collected on many individual devices and being used for a Machine Learning model by sending only the local updates and keeping the data. At the same time, Federated Learning heavily relies on the computational and communicational capabilities of the devices that calculate the updates and send them to the main server to be integrated into a global model using one or the other Aggregation Scheme, which is one of the most important aspects of the Federated Learning. Carefully choosing how to aggregate local updates can diminish the impacts present from a huge variety of devices. Therefore, this thesis work presents a thorough investigation of the Aggregation Schemes and analyzes their behaviors in heterogeneous Federated Learning scenarios. It provides an extensive description of the main features of schemes studied, defines the evaluation criteria, presents the resource costs associated with computational and communicational resources of the devices, and shows a fair assessment.