## 2 Related Work
### Improvement of Privacy-Utility Trade-Off  
This paper identifies one of the most significant problems in federated learning, which is privacy preservation, and the trade-off between model utility and security. Thus, methods such as Differential Privacy and Secure Multi-Party Computation that improve privacy introduce the deterioration of model quality and higher computational overhead as their trade-offs. Study [30] analyzed this trade-off when applying federated learning to the healthcare sector and showed that, while it is possible to provide robust privacy protection, the decline in model accuracy is a major issue. Study [31] recently developed an optimization approach to reducing the privacy-utility trade-off and suggested that rather than adding some noise to each gradient, one can adopt Aggregated Gradient Perturbation. This approach helps in minimizing the amount of noise that is fed into the model while at the same time ensuring privacy preservation. The Novel Aggregated Gradient Perturbation Mechanism proposed in this paper extends these ideas by presenting a new efficient manner to decrease the noise used when updating the model in high-dimensional healthcare datasets while increasing utility and maintaining privacy. The healthcare data collaboration field can be considered as the starting point of a new age, demonstrating the possibility of improving the quality of patient care and speeding up the development of new medical technologies. However, a significant problem has appeared in the protection of personal information considered to be confidential, posing certain difficulties. This paper proposes a new solution to these challenges by developing privacy-preserving federated learning models to overcome the difficulties in this complex field. The solution allows healthcare organizations to train machine learning models on data located in different places and, at the same time, protect the confidentiality of patient data described in [32]. In the model aggregation phase, the mechanism ensures the privacy of the data by employing state-of-the-art techniques including Secure Multi-Party Computation and Differential Privacy. In order to assess the efficiency of the proposed solution, a number of simulations and evaluations were performed, including accuracy, time consumption, and privacy preservation. The findings showed that this approach was indeed more effective than the other available methods and offered higher value with strong privacy protection. In this regard, the approach reflected the possibility of secure and privacy-preserving cooperation in the context of healthcare data sharing, proving the applicability and efficiency of the approach. Federated learning (FL) was introduced as a novel collaborative AI technique that allowed the distributed training of models without the transfer of data, thus incorporating privacy into the system. But it was crucial to understand that FL could not completely solve the problem of preserving confidentiality in AI and ML models. Nevertheless, most of the previous works did not present results related to privacy measurement when using FL, and the reason for this was that it was considered that privacy was protected by the design of FL. This could also be due to the fact that the concept of privacy measurement metrics and methods is not very well understood. Study [22] presented a systematic review of privacy measurement in FL in terms of assessing the efficiency of protecting Sensitive Data Privacy during the training of AI and ML models. However, FL seemed to be a viable approach for protecting privacy in model training, and it was crucial to verify that privacy was indeed protected. The survey therefore presented a review of the current state of privacy measurement in FL, identified research gaps, and proposed future research directions. In addition, the paper also had a case study that compared the efficiency of the discussed privacy measures in a given FL environment.