Federated Learning (FL) enables the training of machine learning models over distributed data on many devices simultaneously without relocating any sensitive information to a centralized server. The system’s privacy concerns pose another important obstacle, especially when it comes to heavily sensitive data. In this paper, we investigate the enhancement of privacy protection using Federated Learning in conjunction with Differential Privacy (DP), which is generally used to protect the accuracy of the given models. We study methods of implementing DP into the federated learning workflow, including noise injection into gradients, secure model update aggregation, and privacy budget allocation. We further analyze the impact of these techniques of preserving privacy on the quality of the global model, particularly on its accuracy. By using real-world datasets for experimentation, we demonstrate the trade-off between maintaining privacy and achieving satisfactory performance on the model. These findings are intended to inform the design of more robust and secure federated learning systems by providing guidance on making privacy-accuracy trade off decisions

FL, DP, Privacy Preservation, Decentralized Data, Model Accuracy, Privacy-Preserving Machine Learning, Secure Aggregation, Privacy Budget, Model Training, PPTAP

In other words, privacy preservation in decentralized data situations is a very clever balancing act and FL + DP is a favourable alternative to build efficient models without sacrificing machine learning privacy requirements. However, in spite of the considerable advances achieved with the integration of such techniques, substantial hurdles remain, particularly within the issue of privacy vs. accuracy. Such trade-off is significant, and privacy-preserving techniques lead to considerable loss in model performance. In addition, dynamic privacy budgets can be difficult to calculate, data may be heterogeneous and non-IID, making the management of FL systems a challenging task, and hybrid privacy-preserving methods can be computationally expensive. Nevertheless, in spite of these problems, the prospects of Differentially Private Federated Learning remain promising, particularly with the fading off of these issues, more specifically the advancement of adaptive privacy techniques and the dynamic budget management, and a need to enhance computational efficiency. Research needs to transition now towards improving and optimizing those systems, to be robust and applicable in real world scenarios such as healthcare, finance and mobile services. But with the related cryptographic technologies and Federated Learning model convergence continuously researched, it is able to realize a practical, economical, and robust protection scheme in many decentralized machine learning scenarios

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