A Federated Learning Model for Privacy Protection Based on Blockchain and Dynamic Evaluation

While federated learning is widely used as a privacy-preserving technology, new challenges such as instability of the central server and privacy leakage caused by the interaction between the federated learning server and the participants as well as security issues have arisen. A privacy-preserving federated learning model based on blockchain and dynamic evaluation is proposed. Local training is performed using sparsification, and global model updates are protected using differential privacy to solve the privacy leakage problem in the federated learning process, using digital signature and double Hash comparisons to verify the identity of the participants and the ownership of the trained model after the completion of local training. In addition, multiple weight dynamic evaluation method is used to calculate the model’s single-round evaluation and the final evaluation of the participants. Experiments show that the proposed model can effectively solve the single-point failure and local model verification problems in federation learning, and the use of sparsification and differential privacy can guarantee the security of the model with a slight loss of accuracy compared with traditional federation learning. The evaluation of the local model and the participants are scored as the basis for the participants’ contributions, thus effectively ensuring the fairness of the incentive mechanism.