Federated learning is an innovative, decentralized machine learning paradigm that allows multiple devices or entities to collaboratively train a shared model without transferring data to a central server. By keeping data localized, this distributed approach ensures enhanced privacy and security for each participating node. Facial recognition, a rapidly evolving field, leverages deep learning techniques to achieve remarkable advancements, often surpassing human-level performance on certain datasets. However, the sensitive nature of facial data, which contains personally identifiable information, raises significant privacy and security concerns. Federated learning has emerged as a promising solution to address these privacy challenges in the facial recognition community. This paper presents a comprehensive review of existing literature on facial recognition frameworks utilizing federated learning. The reviewed techniques are systematically categorized to provide a structured analysis, emphasizing their contributions and relevance to the broader domain of federated learning-based facial recognition. Specifically, this work aims to summarize and analyze various federated learning-based facial recognition methods, their underlying techniques, and their objectives. Furthermore, it offers a high-level perspective on how different functionalities and design principles of federated learning have been applied in facial recognition applications. By doing so, this review identifies key challenges and highlights promising research directions for future advancements in the field.
Graph Neural Networks (GNNs) have demonstrated remarkable performance across various domains, but face significant limitations in deeper architectures due to the oversmoothing problem—where node representations become increasingly indistinguishable through successive graph convolution operations. This paper proposes a novel information-theoretic approach that primarily addresses oversmoothing through Rényi entropy optimization. Our method quantifies and maximizes the diversity of node representations across network layers using kernel density estimation with Gaussian kernels. By formulating a graph-structured entropy regularization term that respects the underlying topology, we encourage networks to maintain discriminative features while preserving essential structural information. This approach integrates seamlessly with existing GNN architectures, requiring minimal modifications to the training procedure. Extensive experiments on benchmark datasets demonstrate that our Rényi entropy regularization consistently improves performance across multiple GNN variants, with particularly significant gains in deeper architectures where oversmoothing is most problematic. The results show that maintaining representation diversity through entropy maximization effectively counters the homogenization tendency of deep GNNs, establishing a principled foundation for developing more robust and expressive graph neural networks.
The use of Electric Vehicles (EV) has increased in recent years. The autonomy of the EV, expressed as its Driving Range (DR) is a key factor. This autonomy depends on several variables related to the vehicle itself as well as with external conditions. An accurate estimation of the DR value at each moment is a challenging task. In this paper, we build a dataset with 11 features, for DR estimation, using publicly available EV data. Then, we discuss the use of Machine Learning (ML) regression techniques to estimate DR, with Linear Regression (LR), Multilayer Perceptron (MLP), and Radial Basis Function (RBF) neural networks. Moreover, we assess the effect of unsupervised dimensionality reduction techniques using feature selection and feature reduction approaches. The experimental results show that the use of both feature selection and feature reduction are useful at reducing the dimensionality of the data, keeping or improving the performance for DR estimation. This study also identifies a few top features for DR estimation.
The increasing connectivity of Internet of Medical Things (IoMT) devices has accentuated their susceptibility to cyberattacks. The sensitive data they handle makes them prime targets for information theft and extortion, while outdated and insecure communication protocols further elevate security risks. This paper presents a lightweight and innovative approach that combines Benford’s law with statistical distance functions to detect attacks in IoMT devices. The methodology uses Benford’s law to analyze digit frequency and classify IoMT devices traffic as benign or malicious, regardless of attack type. It employs distance-based statistical functions like Jensen-Shannon divergence, Kullback-Leibler divergence, Pearson correlation, and the Kolmogorov test to detect anomalies. Experimental validation was conducted on the CIC-IoMT-2024 benchmark dataset, comprising 45 features and multiple attack types. The best performance was achieved with the Kolmogorov test ( α = 0.01 ), particularly in DoS ICMP attacks, yielding a precision of 99.24% , a recall of 98.73% , an F1 score of 98.97% , and an accuracy of 97.81% . Jensen-Shannon divergence also performed robustly in detecting SYN-based attacks, demonstrating strong detection with minimal computational cost. These findings confirm that Benford’s law, when combined with well-chosen statistical distances, offers a viable and efficient alternative to machine learning models for anomaly detection in constrained environments like IoMT.
Equality between men and women has been a long, complex, and silent struggle. As with history in general, societies tend to forget past events easily. Therefore, analysing spoken words and written texts is essential to understanding the context in which works were created and, more broadly, the realities of different historical periods. In this regard, the Spanish Golden Age of Theatre provides a vast, rich, and diverse collection of works that allow for the study of male and female participation, serving as a starting point for analysing gender equality in that era. This paper focuses on a selection of plays written by Lope de Vega, one of the most prolific, influential, writers of the period, with the aim of classifying the characters portrayed in his works. To achieve this pre-trained BERT models were used for this task, yielding accuracy and meaningful results. This approach prides a novel perspective in Lope de Vega's play, achieving a F1-score = 0.98.
