Research on Privacy Protection Technology of “2+2” Verifiable Federated Learning

Secure multiparty computing (MPC) encrypts local models containing sensitive information before aggregation and transmits them to centralized servers, effectively preventing direct information leakage and providing an efficient encryption scheme for distributed systems. However, the server may still indirectly obtain local data through methods such as deep leakage from gradients (DLG). In addition, during the process of data transmission between the server and each client, it is easy to be attacked and tampered with by malicious third parties, resulting in biased data obtained by the client and causing training to not converge. To further enhance security and ensure the correct transmission of data, our research group proposes a "2+2" scheme that uses double decomposition and double aggregation to keep the global model hidden from the server and enable the client to self-verify the transmission of data. Specifically, based on the traditional MPC decomposition, each decomposition share is divided into public and private parts. This dual decomposition preserves the lossless encryption and decryption advantages of standard MPC and ensures that the central server obtains a nonreal global model. Moreover, in terms of aggregation, in addition to aggregating the global model, further aggregation of validation parameters is added to enable clients to locally validate the correctness of the aggregated parameters. Theoretical analysis and experimental results show that this improvement scheme significantly enhances the security and privacy protection of training data with minimal communication, computing, and storage overhead.