Distributed Learning schemes have emerged as promising alternatives to traditional centralized machine learning, especially in 5G environments where data is generated at the edge. This thesis investigates the impact of non-Independent and Identically Distributed (non-IID) data on the performance of various learning schemes, Federated and Decentralized Learning, using the UCI-HAR dataset as a proof-of-concept. We developed a scalable pipeline which is able to generate different non-IID distributions, We develop a modular pipeline capable of generating multiple non-IID distributions, simulating diverse network topologies, and evaluating model performance under varied hyperparameters and distance metrics. Our results show that when the heterogeneity in the data increases, centralized aggregation methods fail to converge rapidly, and over a certain epoch, they are suboptimal solutions.