EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party

Understand

Federated learning allows multiple participants to conduct joint modeling without disclosing their local data.

• Vertical federated learning (VFL) handles the situation where participants share the same ID space and different feature spaces.
• In most VFL frameworks, to protect the security and privacy of the participants' local data, a third party is needed to generate homomorphic encryption key pairs and perform decryption operations.
• In this way, the third party is granted the right to decrypt information related to model parameters.