## 3 Related Work  
This section investigates primarily the application of federated learning for the confidentiality of data. In their latest article, [PERSON] et al. [25] review the use of federated learning and its application in the prediction of disease. They discuss the use of FL for diagnosing FL in the diagnosis of cardiovascular disease, diabetes, and cancer. Quite naturally, with the use of medical data, they stress the need for privacy and confidentiality when dealing with sensible data. They identify other areas, aside from healthcare, where the implementation of FL makes sense, including smart retail, transportation, natural language processing, and finance. When dealing with FL, there is a need to strike a balance between performance and communication cost. [PERSON] et al. [29] consequently evaluated the cost of communication efficiency in FL algorithms. They relied on latency and bandwidth as limitations and proposed the use of the Averaging Algorithm (FedAVG), Sparse Ternary Compression (STC), Communication-Mitigating Federated Learning (CMFL), and Federated Maximum and Mean Discrepancy (FedMMD) to evaluate communication efficiency. All the algorithms were evaluated on the CIFAR and MNIST datasets using a model that is convolutional neural network (CNN)-based. The data were divided in two ways to cater to the independent and identically distributed (IID) scenario and the non-IID scenario. The following parameters were used in the evaluation: client = 100, number of classes = 10, batch size = 20, and participation = 10%. Unfortunately, in this research, none of the algorithms were able to prove the best solution. However, the authors use this work to identify gaps and provide avenues for future research. [PERSON] et al. [12] introduced COLA, a decentralized training algorithm designed to optimize communication efficiency, scalability, and elasticity while also accounting for unreliable and heterogeneous devices to accommodate data changes, while [PERSON] et al. [30] explored approaches for enhancing mini-batch stochastic gradient (SGD) algorithms and presented a novel post-local SGD method that achieves remarkable performance gains compared to training with large batches. These improvements were observed across well-known benchmark datasets, all while ensuring efficiency and scalability. [PERSON] et al. [31] introduced a fully decentralized architecture called P2P FL (peer-to-peer federated learning) to overcome the limitations of classical federated learning. The conventional federated learning approach involves a centralized controller that collects and consolidates training data from all nodes, maintaining a global model on a cloud-based infrastructure across the network. However, the P2P FL architecture deploys nodes throughout the network, allowing them to interact exclusively with their immediate neighbors, thus eliminating the necessity of a centralized controller. This development in P2P federated learning enables nodes to engage with their next-hop neighbors in just two steps. While federated learning (FL) presents a paradigm shift towards preserving data confidentiality, it is not without its challenges and limitations. One significant concern is the delicate balance between performance and communication efficiency. [PERSON] et al. (2018) pointed out that despite employing various algorithms aimed at enhancing communication efficiency, none proved to be the ultimate solution. This suggest that there is a substantial trade-off between algorithmic performance and communication overhead. Furthermore, the reliance on datasets like CIFAR and MNIST, which are relatively simplistic, may not adequately represent the complexity of real-world data, especially in non-IID scenarios, where data distribution is imbalanced across nodes. Moreover, the literature reflects a gap in addressing the complexities of managing sensitive medical data, where the stakes for privacy and accuracy are notably high.