Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption

Understand

Consider two data providers, each maintaining private records of different feature sets about common entities.

• They aim to learn a linear model jointly in a federated setting, namely, data is local and a shared model is trained from locally computed updates.
• In contrast with most work on distributed learning, in this scenario (i) data is split vertically, i.e.
• by features, (ii) only one data provider knows the target variable and (iii) entities are not linked across the data providers.