Privacy-Preserving federated learning in medical diagnosis with homomorphic re-Encryption.

• A federated learning scheme with homomorphic re-encryption is proposed, which collects data from the IoT devices, completes model training through the fog nodes, and finally aggregates the data on the servers. • The proposed scheme can protect the data frome the IoT devices, and solve the problem of high computational cost and storage cost. • It give the security analysis and the experimental results, which show that our scheme are secure and efficient in the real world applications. Unlike traditional centralized machine learning, distributed machine learning provides more efficient and useful application scenarios. However, distributed learning may not meet some security requirements. For example, in medical treatment and diagnosis, an increasing number of people are using IoT devices to record their personal data, when training medical data, the users are not willing to reveal their private data to the training party. How to collect and train the data securely has become the main problem to be resolved. Federated learning can combine a large amount of scattered data for training, and protect user data. Compared with general distributed learning, federated learning is more suitable for training on scattered data. In this paper, we propose a privacy-preserving federated learning scheme that is based on the cryptographic primitive of homomorphic re-encryption, which can protect user data through homomorphic re-encryption and trains user data through batch gradient descent (BGD). In our scheme, we use the IoT device to encrypt and upload user data, the fog node to collect user data, and the server to complete data aggregation and re-encrypting. Besides, the security analysis and experimental results show that our scheme can complete model training while preserving user data and local models. [ABSTRACT FROM AUTHOR]