Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates

Understand

Vertical federated learning (VFL) is an emerging paradigm that allows different parties (e.g., organizations or enterprises) to collaboratively build machine learning models with privacy protection.

• In the training phase, VFL only exchanges the intermediate statistics, i.e., forward activations and backward derivatives, across parties to compute model gradients.
• Nevertheless, due to its geo-distributed nature, VFL training usually suffers from the low WAN bandwidth.
• In this paper, we introduce CELU-VFL, a novel and efficient VFL training framework that exploits the local update technique to reduce the cross-party communication rounds.