Kalman Filter-Based Differential Privacy Federated Learning Method
Source abstract
Abstract
The data privacy leakage problem of federated learning has attracted widespread attention. Using differential privacy can protect the data privacy of each node in the federated learning, but adding noise to the model parameters will reduce the accuracy and convergence efficiency of the model. A Kalman Filter-based Differential Privacy Federated Learning Method (KDP-FL) has been proposed to solve this problem, which reduces the impact of the noise added on the model by Kalman filtering. Furthermore, the effectiveness of the proposed method is verified in the case of both Non-IID and IID data distributions. The experiments show that the accuracy of the proposed method is improved by 0.3–4.5% compared to differential privacy federated learning.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Privacy-Preserving Technologies in Data · Vehicular Ad Hoc Networks (VANETs) · Stochastic Gradient Optimization Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Cyberspace Security and Computer, Hebei University, Baoding 071000, China
Funding
Ministry of Science and Technology of China · National Key R&D Program “Cyberspace Security” Key Project · Natural Science Foundation of Hebei Province
References (23)
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