An Adaptive Differential Privacy Method Based on Federated Learning
This addresses privacy protection in federated learning, but it is incremental as it builds on existing adaptive methods by considering more factors.
The paper tackles the problem of reduced accuracy in federated learning due to uniform privacy budgets in differential privacy, proposing an adaptive method that adjusts the budget based on factors like accuracy and loss. The result is a reduction in privacy budget by about 16% while maintaining similar accuracy.
Differential privacy is one of the methods to solve the problem of privacy protection in federated learning. Setting the same privacy budget for each round will result in reduced accuracy in training. The existing methods of the adjustment of privacy budget consider fewer influencing factors and tend to ignore the boundaries, resulting in unreasonable privacy budgets. Therefore, we proposed an adaptive differential privacy method based on federated learning. The method sets the adjustment coefficient and scoring function according to accuracy, loss, training rounds, and the number of datasets and clients. And the privacy budget is adjusted based on them. Then the local model update is processed according to the scaling factor and the noise. Fi-nally, the server aggregates the noised local model update and distributes the noised global model. The range of parameters and the privacy of the method are analyzed. Through the experimental evaluation, it can reduce the privacy budget by about 16%, while the accuracy remains roughly the same.