Improved Data Privacy with Differential Privacy in Federated Learning
Multiple users may train machine learning models cooperatively using Federated Learning (FL). There is a risk of malicious acquisition of participants' personal data due to the fact that traditional machine learning needs users to provide data for training. Through the use of federated learning, which involves moving the training process from a central server to terminal devices, users' data may be protected. Each participant keeps their dataset local and only exchanges model updates. This research proposed an innovative proposal for the medical industry's differentiated privacy approach for overcoming these problems. When several healthcare organizations work together to develop models that use different and extensive information, clinical applications may be greatly enhanced. Thus, the Local and Centre Differential Privacy (LCDP) on clinical datasets is a feature of our proposed approach. Reason being that the training data is the primary emphasis of the local model, while the machine learning model is the primary focus of the central model. We discover that the local model and the central model are linked in a unique way, changes in the original data lead to changes in the gradient, which in turn lead to changes in the model parameters. Based on this finding, our technique is better than prior central methods since it protects the data, gradient, and model all at once by bridging the gap between the two. Our system provides better privacy protections and even higher performance than some of the best previous central methods, which is an excellent outcome of rigorous evaluation.