Privacy‐preserving federated learning based on multi‐key homomorphic encryption

With the advance of machine learning and the Internet of Things (IoT), security and privacy have become critical concerns in mobile services and networks. Transferring data to a central unit violates the privacy of sensitive data. Federated learning mitigates this need to transfer local data by sharing model updates only. However, privacy leakage remains an issue. This paper proposes xMK-CKKS, an improved version of the MK-CKKS multi-key homomorphic encryption protocol, to design a novel privacy-preserving federated learning scheme. In this scheme, model updates are encrypted via an aggregated public key before sharing with a server for aggregation. For decryption, a collaboration among all participating…

• NN
  National Natural Science Foundation of China
• AO
  Academy of Finland
• NK
  National Key Research and Development Program of China
• KR
  Key Research and Development Projects of Shaanxi Province