# Kalman Filter-Based Differential Privacy Federated Learning Method
## 5 Conclusions  
In this paper, a Kalman filter-based differential privacy federated learning method, KDP-FL, was proposed. Federated learning was used to achieve data sharing while preserving privacy. In addition, differential privacy was added to federated learning, which can resist gradient differential attacks and further protect user privacy. Furthermore, the influence of the noise added by the differential mechanism on the accuracy of the model was reduced by the Kalman filter. The experiment results and analysis showed that the proposed method was able to train models with little loss of accuracy while protecting user privacy. KDP-FL is suitable for training privacy-preserving models under multiple nodes and can help in data cooperation among multiple nodes. When the neural network is simple, the additional computational overhead brought by Algorithm 3 is negligible. However, when the neural network is complex with a large number of parameters, optimizing all parameters requires calculating the Kalman gain for each parameter, which will bring about a non-negligible additional computational overhead. In addition, the proposed method used Kalman filtering, which increases the computational overhead and is not applicable to devices and nodes with weak computational power, which is a direction for future improvement. Conceptualization, X.Y. and Z.D. ; data collection and analysis, Z.D. ; validation, X.Y. ; writing--original draft preparation, Z.D. ; writing--review and editing, X.Y. and Z.D. All authors have read and agreed to the published version of the manuscript. This work was supported by Ministry of Science and Technology of China, National Key R&D Program \"Cyberspace Security\" Key Project, 2017 YFB0802305 and the Natural Science Foundation of Hebei Province, F2021201052. This work was supported by Ministry of Science and Technology of China, National Key R&D Program* _[PERSON] et al. (2020)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A homomorphic-encryption-based vertical federated learning scheme for rrick management. _Comput. Sci. Inf. Syst._**2020**, _17_, 819-834. [CrossRef]
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