Identifying the right use cases for federated learning
One of the top reasons data science projects fail is lack of access to the right data. This is a shame, because in most cases the right data exists, but the data scientists just can't get access to it. In federated learning, models are trained on decentralized datasets - that is, the data resides in two or more separate databases and never needs to be moved. Federated learning solves problems with data access by enabling machine learning across distributed data. But before investing in federated learning, you need to know when to use it.
