Federated Learning (FL) is a distributed Machine Learning approach that enables the training of neural networks across multiple devices (i.e., clients) owning privacy-protected data. In FL, only model parameters are exchanged during training, ensuring that sensitive user data remains protected. However, in real-world scenarios, the clients’ data often exhibits non-i.i.d. characteristics, stemming from individual behaviors and geographical locations, which can, in turn, lead to slower and unstable optimization processes. Recent research has addressed this challenge by focusing on the loss landscape, drawing from the connection between converging toward flatter minima and achieving better generalization. Specifically, it was shown how leveraging Sharpness-Aware Minimization (SAM) during local training, which aims at finding minima having both low loss value and low sharpness, enables federated global models to reach optimal results.