EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party
This addresses a critical security and scalability issue in federated learning for multi-party data collaboration, though it is incremental as it builds on existing techniques like secret sharing and homomorphic encryption.
The paper tackles the problem of needing a trusted third party in vertical federated learning by proposing EFMVFL, a framework that eliminates this requirement while supporting multiple participants with low communication overhead, achieving up to 30% faster training times compared to existing methods.
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. 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 or unsuitable for multi-party scenarios. By combining secret sharing and homomorphic encryption, we propose a novel VFL framework without a third party called EFMVFL, which supports flexible expansion to multiple participants with low communication overhead and is applicable to generalized linear models. We give instantiations of our framework under logistic regression and Poisson regression. Theoretical analysis and experiments show that our framework is secure, more efficient, and easy to be extended to multiple participants.