The use of data-hungry deep learning algorithms to augment the performance of cloud-deployed software services calls for the constant relaying of private user data over a network that is susceptible to attack from malicious agents and also limits the extent to which data can be collated in a central repository to train centralized machine learning models. To encourage anonymized and decentralized training of models in such scenarios, We propose CrossPriv, a user-privacy sensitive model that enlists the characteristics of cross-silo federated software deployed across the clients participating in the cross-silo FL learning setup. We simulate the efficacy of the model by demonstrating the training of a deep learning model that can detect Pneumonia using X-Rays using training data hosted at two completely different silos, without sharing their raw data. We specify the client and server-side features of the deployed service whilst clearly defining the pipeline of cross-silo federated learning architecture.