# Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
## 2 Related Works  
### Federated Learning  
The literature survey gives an introduction to FL, challenges faced by FL, and various applications of FL. FL [7; 8] is a collaborative machine learning approach where models are trained across multiple devices or servers in a decentralized manner. Instead of sharing data, each client independently performs training on its data and transmits the resulting weights to a server. The aggregation of all the weights from the clients is performed by the server using the Federated Averaging (FedAvg) algorithm to generate a global model. This approach helps to maintain data privacy, as sensitive data do not need to leave the client devices. In certain federated learning studies, the same dataset is partitioned and distributed among multiple clients. While this method is commonly employed in experimental setups, it may not be well-suited for real-world applications [3]. A comparison of FL to centralized learning was carried out [2] and the study demonstrates that the centralized approach delivers superior accuracy in IID setting. Federated learning faces several challenges, including communication overhead, privacy and security concerns, and data distribution issues. One of them is the high communication cost associated with transmitting model weights between clients and the server. This can be exacerbated when large models are involved or when clients are frequently disconnected from the training process due to connectivity issues. While federated learning aims to preserve data privacy, certain vulnerabilities may still exist, particularly when it comes to adversarial attacks or issues related to data leakage during the aggregation process [9]. In many studies, data distribution among clients is considered to be uniformly distributed (IID), which does not reflect real-world scenarios. Non-uniformly distributed (Non-IID) data distribution, where data are uneven or imbalanced across clients, can lead to poorer model performance. This issue has been observed in medical image classification [10], where different clients had varying distributions of labels, affecting model accuracy.  
Federated learning has diverse applications, including areas such as healthcare, facial expression recognition, medical image classification, and binary supervised classification. FL has shown great potential in healthcare [11], as it enables the training of models while preserving patient data privacy. Studies have explored how FL can be applied in healthcare data to avoid the high costs and risks associated with centralized data storage. A healthcare-focused federated learning architecture helps to preserve data privacy by keeping sensitive data localized on clients, while the central server only aggregates model updates. A federated learning model was proposed for facial expression recognition in advertisements, allowing a system to predict user interests in real time [12]. A graphical user interface (GUI) was developed to facilitate this real-time prediction, with suggestions to refine feature extraction techniques for better model performance. FL has been applied to medical image classification [10], showcasing its effectiveness in handling distributed datasets. In a federated SVM architecture [13], it was utilized for binary classification tasks using the MNIST and COVID-19 datasets, with results compared to centralized approaches. These comparisons highlighted the limitations of using random data splits across clients, which may not reflect practical real-world scenarios.