Secure Federated Learning Scheme Based on Secret Sharing and Homomorphic Encryption in Smart Healthcare

Federated learning enhances data sharing and collaboration between healthcare institutions, thereby improving the accuracy and efficiency of medical diagnoses, treatments, and predictions. However, existing federated learning solutions face security and efficiency challenges. Model parameter updates during training may inadvertently disclose information about local training datasets. To ensure parameter confidentiality, researchers have proposed various solutions such as masking protocols and differential privacy. However, masking protocols often lack strong security, whereas differential privacy leads to tradeoffs between accuracy and privacy. To address these challenges, this study proposes a secure federated learning scheme for smart healthcare based on secret sharing and homomorphic encryption. This scheme effectively prevents both healthcare clouds and clients from stealing model parameters and resists collusion attacks among participants. In addition, a ciphertext verification algorithm is used to ensure that model parameters can be verified during training. Security and performance analyses demonstrate that our scheme meets the confidentiality and integrity requirements for model parameters in smart healthcare scenarios, with significant improvements in computational and transmission efficiency compared to existing solutions.