Abstract
Energy smart grids and other modern industrial control systems networks impose considerable security management challenges due to several factors: their broad geographic dispersion and capillarity, the constrained nature of many of the devices and network links that integrate them, and the fact that they are often fragmented across multiple domains, owned and managed by different entities which often have non-aligned or even competing interests. Due to this scenario, we propose to improve federated learning-based anomaly detection for smart grids and other industrial control networks, using a federated data-centric methodology that attends to the balance and causality of the data, improving the representation of the different classes of anomalies of the ingested data, which directly impact the classifier's performance. The proposed approach shows up to 33% performance improvements in terms of F1-score for attack classification, compared to the baseline federated approach (not attending to class imbalance and causality) on a broad range of industrial control systems traffic datasets.

Abstract
Group fairness in machine learning is an important area of research focused on achieving equitable outcomes across different groups defined by sensitive attributes such as race or gender. Federated learning, a decentralized approach to training machine learning models across multiple clients, amplifies the need for fairness methodologies due to its inherent heterogeneous data distributions that can exacerbate biases. The intersection of federated learning and group fairness has attracted significant interest, with 48 research works specifically dedicated to addressing this issue. However, no comprehensive survey has specifically focused on group fairness in Federated Learning. In this work, we analyze the key challenges of this topic, propose practices for its identification and benchmarking, and create a novel taxonomy based on criteria such as data partitioning, location, and strategy. Furthermore, we analyze broader concerns, review how different approaches handle the complexities of various sensitive attributes, examine common datasets and applications, and discuss the ethical, legal, and policy implications of group fairness in FL. We conclude by highlighting key areas for future research, emphasizing the need for more methods to address the complexities of achieving group fairness in federated systems.

Abstract

Abstract
Capsule endoscopy has emerged as a non-invasive alternative to traditional gastrointestinal inspection procedures, such as endoscopy and colonoscopy. Removing sedation risks, it is a patient-friendly and hospital-free procedure, which allows small bowel assessment, region not easily accessible by traditional methods. Recently, deep learning techniques have been employed to analyse capsule endoscopy images, with a focus on lesion classification and/or capsule location along the gastrointestinal tract. This research work presents a novel approach for testing the generalization capacity of deep learning techniques in the lesion location identification process using capsule endoscopy images. To achieve that, AlexNet, InceptionV3 and ResNet-152 architectures were trained exclusively in normal frames and later tested in lesion frames. Frames were sourced from KID and Kvasir-Capsule open-source datasets. Both RGB and grayscale representations were evaluated, and experiments with complete images and patches were made. Results show that the generalization capacity on lesion location of models is not so strong as their capacity for normal frame location, with colon being the most difficult organ to identify.

Abstract
Missing data is a common challenge in real-world datasets and can arise for various reasons. This has led to the classification of missing data mechanisms as missing completely at random, missing at random, or missing not at random. Currently, the literature offers various algorithms for imputing missing data, each with advantages tailored to specific mechanisms and levels of missingness. This paper introduces a novel approach to missing data imputation using the well-established label propagation algorithm, named Label Propagation for Missing Data Imputation (LPMD). The method combines, weighs, and propagates known feature values to impute missing data. Experiments on benchmark datasets highlight its effectiveness across various missing data scenarios, demonstrating more stable results compared to baseline methods under different missingness mechanisms and levels. The algorithms were evaluated based on processing time, imputation quality (measured by mean absolute error), and impact on classification performance. A variant of the algorithm (LPMD2) generally achieved the fastest processing time compared to other five imputation algorithms from the literature, with speed-ups ranging from 0.7 to 23 times. The results of LPMD were also stable regarding the mean absolute error of the imputed values compared to their original counterparts, for different missing data mechanisms and rates of missing values. In real applications, missingness can behave according to different and unknown mechanisms, so an imputation algorithm that behaves stably for different mechanisms is advantageous. The results regarding ML models produced using the imputed datasets were also comparable to the baselines.

Abstract
Mammography imaging is the gold standard for breast cancer detection and involves capturing two projections: mediolateral oblique and craniocaudal projections. The implementation of an approach that allows the acquisition of only one projection and reconstructs the other could mitigate patient burden, minimize radiation exposure, and reduce costs. Image-to-image translation has showcased the ability to generate realistic synthetic images in different medical imaging modalities which make these techniques a great candidate for the novel application in mammography. This study aims to compare five image-to-image translation approaches to assess the feasibility of reconstructing a mammography projection from its counterpart. The results indicate that ResViT shows the best overall performance in translating between both projections.