The purpose of this work is to segment muscles in MRI volumes of lower limbs, comparing the performances of two different techniques: a 3D neural network, trained locally on a manually segmented dataset, and DAFNE (Deep Anatomical Federated Network), an open-source software trained in a decentralized collaborative manner using federated learning. Federated learning is a novel technique that involves different clients that collaborate without sharing the data. Single models are trained locally by the centers. These models are then aggregated together on the server side and the updated model is shared with each center. The dataset has been granted by Radbound University Medical Center and contains 166 lower limb volumes from patients with two different pathologies, Fascioscapulohumeral muscular Distrophy (FSHD) and Myotonic Dystrophy Type 1 (MD1), and healthy volunteers acquired using the Dixon technique.