To solve the problem that gradient updating leads to the possible leakage of local data in federated learning,federated ensemble algorithms based on local desensitiza-tion data are proposed.The algorithm desensitizes the raw data with different values of variability and fitness thresholds,employing diverse models for local training on data with different desensitization levels to ascertain parameters suitable for a federated ensemble approach.Experimental results show that the stacking federated ensemble algorithm and voting federated integration algorithm outperform the baseline accuracy achieved by the federated average algorithm with traditional centralized training.In practical applications,different desensitization parameters can be set according to different needs to protect data and improve its security.
维普
万方数据