In this work, we propose a Federated Learning (FL) method combining an ensemble of one-layer neural networks whose optimal parameters can be obtained through a non-iterative procedure. Therefore, unlike most state-of-the-art methods, the collaborative global model can be obtained using a single round of communication between all clients of the federated scheme. It presents a computationally efficient and incremental batch aggregation process that suits the needs of a realistic federated scenario, simplifying the management of the federated training process. The model provides the same performance in identically and non-identically distributed data scenarios. Besides, the model implements a Fully Homomorphic Encryption (FHE) scheme to enhance robustness against privacy leaks or attacks, enabling clients to offload computational work to the coordinator, which operates entirely on encrypted data. We achieve an efficient and secure distributed model with an improved representation capacity for this type of architecture. The source code used in the study is made publicly available.

In the realm of cybersecurity, the detection of network intrusions stands as a paramount challenge, with ever-evolving threats demanding innovative solutions.This study delves into the application of diverse machine learning algorithms on a contemporary dataset (UGR'16) comprising real-world instances of intrusion in software systems.Specifically, several Machine Learning models (Outlier Detectors, Ensemble Methods, Deep Learning, and Conventional Classifiers) were tested and compared with previously reported results using a standard methodology.The obtained results reveal that the Ensemble Methods have been capable of improving the results from prior research.Particularly, the Extreme Gradient Boosting (XGBoost) algorithm offers better results than the original solution with Random Forest, with an AUC of 0.9218 as opposed to 0.8977, and more than four times as fast for the problem to solve.

Abstract Any machine learning (ML) model is highly dependent on the data it uses for learning, and this is even more important in the case of deep learning models. The problem is a data bottleneck, i.e. the difficulty in obtaining an adequate number of cases and quality data. Another issue is improving the learning process, which can be done by actively introducing experts into the learning loop, in what is known as human-in-the-loop (HITL) ML. We describe an ML model based on a neural network in which HITL techniques were used to resolve the data bottleneck problem for the treatment of pancreatic cancer. We first augmented the dataset using synthetic cases created by a generative adversarial network. We then launched an active learning (AL) process involving human experts as oracles to label both new cases and cases by the network found to be suspect. This AL process was carried out simultaneously with an interactive ML process in which feedback was obtained from humans in order to develop better synthetic cases for each iteration of training. We discuss the challenges involved in including humans in the learning process, especially in relation to human–computer interaction, which is acquiring great importance in building ML models and can condition the success of a HITL approach. This paper also discusses the methodological approach adopted to address these challenges.

In this work we applied Curriculum Learning (CL) to evaluate the performance of a machine learning (ML) model for pancreatic cancer prediction.As the dataset required it, we applied missing value imputation and data augmentation techniques.We compare different curriculum configurations in terms of pacing functions and we perform different experiments concluding that CL helps to train the ML model.Nevertheless, not all the configurations behave in the same way, and the best results were obtained when organising the curriculum in increasing levels of difficulty following exponential pacing.

Human-in-the-Loop Machine Learning (HITL-ML) is a set of techniques that attempt to actively introduce experts into the learning loop of machine learning (ML) models to improve the learning process. In this paper we present a HITL-ML strategy for the treatment of pancreatic cancer in which a classifier should decide whether a chemotherapy treatment is suitable or not for the patient. The contribution of this work is, first, to demonstrate that involving human experts in the learning process improves the learning capacity of the model; second, to develop a relatively novel Interactive Machine Learning (IML) approach in which unstructured feedback obtained from the experts is used to optimize the synthetic cases generator implemented by a Generative Adversarial Network (GAN). This GAN is used to augment the dataset and to improve the generalization capabilities of the model. Finally, the inclusion of humans in the learning process also poses new challenges, e.g., aspects related to Human-Computer Interaction (HCI), normally irrelevant in ML systems, are now of great importance and can condition the success of a HITL approach. This paper also discusses the approach taken to address these challenges.

Federated learning (FL) is a distributed approach to developing collaborative learning models from decentralized data. This is relevant to many real applications, such as in the field of the Internet of Things, since the models can be used in edge computing devices. FL approaches are motivated by and designed to protect privacy, a highly relevant issue given current data protection regulations. Although FL methods are privacy-preserving by design, recently published papers show that privacy leaks do occur, caused by attacks designed to extract private data from information interchanged during learning. In this work, we present an FL method based on a neural network without hidden layers that incorporates homomorphic encryption (HE) to enhance robustness against the above-mentioned attacks. Unlike traditional FL methods that require multiple rounds of training for convergence, our method obtains the collaborative global model in a single training round, yielding an effective and efficient model that simplifies management of the FL training process. In addition, since our method includes HE, it is also robust against model inversion attacks. In experiments with big data sets and a large number of clients in a federated scenario, we demonstrate that use of HE does not affect the accuracy of the model, whose results are competitive with state-of-the-art machine learning models. We also show that behavior in terms of accuracy is the same for identically and non-identically distributed data scenarios.

In this study, we analyze the capability of several state of the art machine learning methods to predict whether patients diagnosed with CoVid-19 (CoronaVirus disease 2019) will need different levels of hospital care assistance (regular hospital admission or intensive care unit admission), during the course of their illness, using only demographic and clinical data. For this research, a data set of 10,454 patients from 14 hospitals in Galicia (Spain) was used. Each patient is characterized by 833 variables, two of which are age and gender and the other are records of diseases or conditions in their medical history. In addition, for each patient, his/her history of hospital or intensive care unit (ICU) admissions due to CoVid-19 is available. This clinical history will serve to label each patient and thus being able to assess the predictions of the model. Our aim is to identify which model delivers the best accuracies for both hospital and ICU admissions only using demographic variables and some structured clinical data, as well as identifying which of those are more relevant in both cases. The results obtained in the experimental study show that the best models are those based on oversampling as a preprocessing phase to balance the distribution of classes. Using these models and all the available features, we achieved an area under the curve (AUC) of 76.1% and 80.4% for predicting the need of hospital and ICU admissions, respectively. Furthermore, feature selection and oversampling techniques were applied and it has been experimentally verified that the relevant variables for the classification are age and gender, since only using these two features the performance of the models is not degraded for the two mentioned prediction problems.

Spectral clustering is a clustering paradigm that has been shown to be more effective in finding clusters with non-convex shapes than some traditional algorithms such as k-means.However, this algorithm is not directly applicable when the data is naturally distributed in different locations, as it happens in many Internet of Things scenarios.In this work, we propose a distributed spectral clustering to create a cooperative federated model to deal with those cases in which the data is distributed in different sites and with data privacy concerns.We demonstrate that sharing a minimal amount of information allows this distributed version of the spectral clustering to achieve good behavior for clustering several synthetic data sets.

Sleep staging is one of the most important tasks on the context of sleep studies. For more than 40 years the gold standard to the characterization of patient’s sleep macrostructure was the set of rules proposed by Rechtschaffen and Kales (R&K) recently modified by AASM rules. Nevertheless the resulting map of sleep, the so-called hypnogram, has several limitations such as its low temporal resolution and the unnatural characterization of sleep through assignment of discrete sleep states. This study reports an automatic method for the characterization of the structure of the sleep. The method is based on the use of fuzzy inference in order to provide soft transitions among the different states. Main intention is to overcome limitations of epoch-based sleep staging by obtaining a more continuous evolution of the sleep of the patient.