I'm interested in whether federated learning can bring ML to situations where you don't want to pool all the data in one spot for privacy reasons. Say you run a B2B Saas business in seperate tenancies and in each tenant contains sensitive information about that client (eg about their clients). Could you run a federated learning model such that it could learn in each tenant and improve the overall model but not share any of the sensitive information between tenants?