Efficient and Secure Model Initialization Scheme in Federated Learning

Abstract

In Federated Learning (FL), a shared initial global model is distributed by an aggregator through a cloud server to all participating clients for the purpose of local training. To mitigate the threat of a potentially malicious cloud server, consistency verification of the client-received initial model is essential. Public key encryption with equality test (PKEET) enables authorized entities to determine whether two ciphertexts contain an identical message or not without decryption. This property supports consistency verification of the initial global model in FL settings. Identity-based encryption with equality test (IBEET) eliminates the certificate, resolving certificate management issues in PKEET. SM9, as a Chinese identity-based cryptographic standard, has been applied in many fields. However, existing equality test schemes based on SM9 only support equality tests on two ciphertexts, resulting in linear executions of the test algorithm when performing tests on multiple ciphertexts. To enhance efficiency and functionality, we propose an efficient multi-ciphertext equality test scheme based on SM9 (SM9-MET), which is well-suited for verifying the initial model in FL. The proposed SM9-MET scheme supports simultaneous equality testing on multiple ciphertexts while maintaining constant-size ciphertexts. Our method requires only one single execution of the test algorithm. Under the $q$-BDHI assumption in the random oracle model, we prove that the SM9-MET scheme achieves one-way security and indistinguishability against chosen-plaintext attacks. Finally, we compare the SM9-MET scheme to other related schemes, and the results demonstrate considerable advantages in both encryption and equality tests on multiple ciphertexts.