Implementing differential privacy in machine learning presents challenges, particularly in balancing privacy with model accuracy and utility. Adding noise to protect privacy can impact the accuracy of models, especially in complex tasks. Managing this trade-off requires careful calibration of privacy parameters. Additionally, the repeated access to data during model training can accumulate privacy loss, necessitating advanced techniques to monitor and control this cumulative effect. These challenges underscore the need for innovative approaches in applying differential privacy in machine learning, ensuring robust privacy protection without significantly compromising the effectiveness of the models.