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Federated learning allows multiple participants to conduct joint modelingwithout disclosing their local data. Vertical federated learning (VFL) handlesthe situation where participants share the same ID space and different featurespaces. In most VFL frameworks, to protect the security and privacy of theparticipants' local data, a third party is needed to generate homomorphicencryption key pairs and perform decryption operations. In this way, the thirdparty is granted the right to decrypt information related to model parameters.However, it isn't easy to find such a credible entity in the real world.Existing methods for solving this problem are either communication-intensive orunsuitable for multi-party scenarios. By combining secret sharing andhomomorphic encryption, we propose a novel VFL framework without a third partycalled EFMVFL, which supports flexible expansion to multiple participants withlow communication overhead and is applicable to generalized linear models. Wegive instantiations of our framework under logistic regression and Poissonregression. Theoretical analysis and experiments show that our framework issecure, more efficient, and easy to be extended to multiple participants.

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