Federated Learning (FL) is a privacy-preserving machine learning strategy that enables network-edge clients to train a shared model under the orchestration of a central server. This thesis deals with the design and analysis of FL systems. Specifically, the focus is on three primary design choices, which have a predominant effect on the communication and operational costs of an FL system: (i) the communication protocol used for the interactions among the server and the clients; (ii) the database employed to store the shared model parameters; (iii) the internal architecture of the system to handle synchronization and model update processes. Identifying the most efficient and low-cost configurations requires the availability of ad-hoc simulations and profiling tools able to emulate different implementation solutions and assess their performance under variable load profiles.