## 2 The Papers
### Privacy-Preserving Learning  
Deep Learning heavily relies on existing and forthcoming patient data to yield precise and dependable outcomes within the realm of healthcare applications. Despite the copiousness of biomedical data, its dissemination and retrieval are hindered by ethical limitations, particularly concerning safeguarded health-related information pertaining to patients. Consequently, the actualization of medical AI systems encounters challenges, as the requisite data for their development and training are ensnared within the confines of hospital security protocols. To engender resilient algorithms, the databases employed for training, validation, and testing must encompass the complete spectrum of pathological deviations and permutations. Additionally, it is imperative to leverage the entirety of accessible information to formulate a more tailored solution. In instances where training datasets lack heterogeneity, there is a propensity for algorithms to exhibit partiality or inclination towards specific patient profiles [2].  
In the field of privacy-preserving learning three papers are presented.  
The first paper presents a novel approach for protecting sensitive data in the healthcare setting, as nowadays the trend is to send it outside the facility for it to be processed, trained on etc. [3]. The researchers designed the solution to make it robust against both human perception, as well as against different software attacks such as AI reconstruction attempts. Therefore, by using the proposed pipeline, the data will be obfuscated before leaving the healthcare facility, and the external processing (such as training of AI models) can be performed with a satisfactory privacy-accuracy trade-off, i.e., without a significant drop in accuracy. The three main objectives of the paper are to hide the content from any person viewing the images, to make it difficult for an AI to reconstruct the image, as well as to facilitate AI model training on such data. Regarding the technical aspects, a Variational Autoencoder (VAE) is used, trained on around 30,000 images from the Medical MNIST dataset. The VAE has two different output channels, one based on the mean of the normal distribution, which offers more privacy, but limits the performance in further ML applications, and a second one which is based on the standard deviation of the normal distribution, which trades privacy for training performance.  
The second paper's focus lies on maintaining and improving the training and prediction accuracy of AI based solutions in heart disease diagnosis, while overcoming data privacy issues [4]. The main mechanism is federated learning, which aims to keep the data on a single device while training via a collaborative system of a shared model. The optimizer framework-the Modified Artificial Bee Colony (M-ABC) has been chosen due to its flexibility and user-friendliness, has less parameters than other algorithms and has a fast convergence rate. These two methods work together, as the federated matched averaging (FedMA) is constructing a privacy-aware framework for a global cloud model, and the M-ABC framework serves as the feature selector on the client's side. The pipeline was trained on the heart disease dataset of UCI Cleveland, with 303 records and 76 attributes.  
In the third paper, the same approach is used, but instead of employing the M-ABC optimization, a hybrid M-ABC with Support Vector Machine is used [5]. The SVM is less prone to overfitting, works well in high dimensional spaces, and has good handling of non-linear data. Moreover, it is a suitable candidate for classifying multiple classes. The M-ABC acts as the feature extractor and the SVM as the classification algorithm. The dataset combines over eleven common features, such as blood pressure, cholesterol serum, sex, age etc. from the datasets of Cleveland, Stalog, Hungary, Long Beach, and Switzerland. As far as results are concerned, the proposed solution is both more efficient and more accurate than the previous ones.