Flexible Vertical Federated Learning with Heterogeneous Parties

Understand

We propose Flexible Vertical Federated Learning (Flex-VFL), a distributed machine algorithm that trains a smooth, non-convex function in a distributed system with vertically partitioned data.

• We consider a system with several parties that wish to collaboratively learn a global function.
• Each party holds a local dataset; the datasets have different features but share the same sample ID space.
• The parties are heterogeneous in nature: the parties' operating speeds, local model architectures, and optimizers may be different from one another and, further, they may change over time.