Privacy preservation-based federated learning with uncertain data

Federated learning (FL) belongs to distributed machine learning. It allows data information sharing between users while protecting their data privacy at the same time. However, in many real-world scenarios, the data collected by client devices may be affected by noise in the working environment, leading to the decreased accuracy and confidence. Therefore, it is necessary to take measures to reduce data uncertainty in order to enhance the performance of FL algorithms. Traditional FL methods encounter challenges in handling uncertain data, which motivates the introduction of multi-view learning in this paper which designs an FL model suitable for highly variable data characteristics. We first achieve information complementarity among data views while ensuring the consistency in data representation. Furthermore, we quantify data uncertainty using the reachable region of data noise, thereby improving model robustness. To maintain data privacy between clients, we design an adaptive Kalman filter-based differential protection security protocol. Clients use the protocol to process local data and upload it to the master server, which returns the updated model parameters to the clients. The experimental results demonstrate the effectiveness of the federated learning model proposed in this paper.