## 2 Related Work
### Improvement of Privacy-Utility Trade-Off
The case study was used as a vehicle to demonstrate the real-life application of the discussed privacy measurements, and therefore, the study provided real-life recommendations that could be used by researchers and practitioners to enhance the privacy of data sharing while at the same time considering other important factors like communication overhead and accuracy. Healthcare data has become a rapidly growing entity and is extremely valuable because it contains information about patients. However, this data and its storage environment have their own unique characteristics which present privacy and security risks. In the course of machine learning processes, people did not want to disclose their information because of privacy issues. To this end, federated learning was developed as a solution that enabled the training of models on private data without the need to share the data. However, the current literature reveals that privacy leakage could still be a threat during the training phase of a federated learning system. To address this problem, a privacy-preserving federated learning scheme with homomorphic encryption (PPFLHE) was proposed in [23]. In particular, we used Homomorphic Encryption technology on the client side to encrypt the training model that users share, so that the model would be secure and users would not be able to see each other's data. Furthermore, to prevent internal threats, Access Control (AC) was used to check the identity of a user and to determine the level of trust that user should be granted. On the server side, an ACK mechanism was developed to expel or ignore idle or unresponsive users occasionally to minimize the waiting time and communication costs, as well as the problem of user dropouts during training. A scalable federated learning (FL) framework was proposed by [24] for interactive smart healthcare systems, focusing on chest X-ray images, to address the issues with limited and episodic clients in remote hospitals with imbalanced data. The framework used a data augmentation technique to address the class imbalance problem for local model training. In real life, some clients may leave the training process while others may join in due to some technical factors or network complications. The method was tested with five to eighteen clients and small and large testing data sizes in order to compare the efficiency of the method in different conditions. Study [33] proposed a novel federated learning framework aimed at optimizing resource scheduling for intelligent edge computing. The framework addressed challenges associated with device heterogeneity, non-IID data distribution, and communication overhead in edge environments. An adaptive client selection mechanism was introduced, taking into account computational capabilities, energy status, and data quality [34]. To effectively manage non-IID data, a personalized model training approach was implemented, utilizing multi-task learning and local batch normalization layers. The framework also incorporated efficient model aggregation techniques and communication-efficient updates to minimize bandwidth consumption. Furthermore, a privacy policy was integrated, distinguishing between privacy and collective security, to enhance data protection. Scheduling problems were developed based on multi-objective optimization, effectively combining computational and communication efficiencies while updating both local and global guidelines. The proposed method achieved a 15% improvement in model accuracy and a 40% reduction in communication overhead compared to state-of-the-art algorithms. In this work, a new framework called Hierarchical Federated Learning (HFL) is proposed for large integrated healthcare organizations which contain many institutions. The architecture of HFL uses a two-layered structure for the aggregation of updates, where local updates are firstly aggregated at the regional level and then at the global level. This approach is used in an attempt to reduce the communication overhead and increase system scalability.