Federated Learning is an emerging paradigm in distributed machine learning that allows AI models to be trained directly on user devices without the need to transfer data to a central server, providing scalability and privacy. However, large-scale networks present significant challenges, such as data heterogeneity and the potential presence of malicious or corrupted agents. This thesis explores the key algorithms and methods that address these challenges and enable versatile, robust, and secure learning. It also introduces FedGW, an innovative algorithm based on a rigorous mathematical framework that aims to identify homogeneous clusters of clients by estimating their interactions through a Gaussian reward mechanism applied to local loss functions.