Interpret Federated Learning with Shapley Values

Understand

Federated Learning is introduced to protect privacy by distributing training data into multiple parties.

• Each party trains its own model and a meta-model is constructed from the sub models.
• In this way the details of the data are not disclosed in between each party.
• In this paper we investigate the model interpretation methods for Federated Learning, specifically on the measurement of feature importance of vertical Federated Learning where feature space of the data is divided into two parties, namely host and guest.