## 1 Introduction  
Federated Learning (FL) is a new approach to machine learning that allows you to train models on data distributed across different locations without the need to centralize them. In this way, the information contained in large quantities of data can be exploited without compromising the privacy and security of the data themselves. Federated Learning (FL) proves to be an ideal paradigm to collaboratively exploit the enormous amounts of data from devices located around the world, the Internet of Things (IoT), while preserving privacy and security. Federated learning (FL) and edge computing are complementary technologies for distributed AI. FL allows edge devices to train models collaboratively, without sending all the data to the cloud. This reduces latency, improves privacy, and increases computational efficiency. In this work we focus on addressing the following Research Questions through the implementation of a distributed and federated machine learning software suite (track changes and periodical updates are available on the repository: [[https://github.com/[PERSON]/DHFLPL](https://github.com/[PERSON]/DHFLPL)]([https://github.com/[PERSON]/DHFLPL](https://github.com/[PERSON]/DHFLPL)), accessed on 14 September 2024):  
* _What are the impacts of heterogeneous environments in Federated Learning?_
* _How can the federated learning scenario benefit from different systems and architectures?_
* _What are the privacy implications of using federated learning?_
* _How can a cluster management system (i.e., Kubernetes) make the application of heterogeneous machine learning models feasible and easier to conduct?_  
We argue that, by replying to these research questions, we could create a clear view of the distributed machine learning scenario. This would highlight the weak points and strengths of such methods. We exploited various methods to generate this kind of system. While distributed and heterogeneous federated learning covers multiple fields such as dedicated algorithms, different architectures and model management, we focus on the analysis of using a federated learning platform with heterogeneous computer architectures. Leveraging cluster management tools, we focused on the analysis of simulating a cluster of devices, which participate in the training of a distributed dataset. This participant uses common methods and models, with the possibility of guaranteeing privacy using a differential privacy algorithm. We explored a number of solutions, which are presented here. To the best of our knowledge, exploring related and similar works is a novel approach to the theme of federated learning. Simulating heterogeneous nodes using Kubernetes clusters and discussing performance outcomes of the combination of ARM and Intel devices. _Federated Learning and Edge Computing in the Internet of Things_  
Ubiquitous Intelligence. Ubiquitous intelligence refers to intelligent systems that are pervasive and ubiquitous, and integrated into the surrounding environment. These systems use sensors, mobile devices, advanced network technologies and artificial intelligence algorithms to provide advanced services and functionality without requiring direct user intervention. The goal is to create an intelligent environment that can anticipate user needs and respond proactively, improving the overall experience. This pervasive intelligence is fundamental in contexts such as smart cities, the Internet of Things (IoT) and new generation networks such as 6G, where the ability to provide services in real-time and in a distributed manner is essential [1; 2]. Containers in Federated Learning. Using containers, such as Docker and Kubernetes, in Federated Learning offers numerous benefits. Docker allows you to create isolated, reproducible environments for each participating node, ensuring consistency and ease of deployment. Kubernetes, on the other hand, facilitates the management of containers at scale, automating the deployment, scaling, and operation of containerized applications.