Federated Learning (FL) enables collaborative training of Machine Learning (ML) models while maintaining user data privacy. However, leaked model updates can reveal private training data. Existing solutions using additive secret sharing introduce intermediary servers, increasing complexity and communication overhead, and often lack privacy guarantees. We propose AddShare+, which enhances efficiency and scalability by creating additive shares for a subset of model weight parameters and using the Elliptic Curve Integrated Encryption Scheme (ECIES) for faster, lighter model encryption. By sampling and splitting a percentage of local weight parameters, AddShare+ reduces computation and communication costs while maintaining model accuracy. We implemented and evaluated AddShare+ on multiple datasets, comparing it with baseline approaches including FedAvg, SCOTCH, FedShare, and AddShare. Results demonstrate that AddShare+ maintains accuracy while significantly reducing running time per round. Notably, sharing as low as 25% of model weights decreases bandwidth demands by over 5x while preserving accuracy within 0.05 % of the full model. Our empirical results demonstrate significant reductions in running time per round with strong privacy guarantees, highlighting the potential of lightweight partial sharing solutions for privacy-preserving FL in resource-constrained environments, paving the way for more efficient and secure collaborative learning systems.

The Bitcoin generator scam is one example of existing deceptive schemes enticing users with promises of free or effortless Bitcoin generation. These scams predominantly exploit individuals unfamiliar with cryptocurrency seeking low-effort avenues to obtain Bitcoin without financial investment. In this paper, we propose and analyze methods to improve the performance of Graph Neural Networks (GNNs) in detecting fraudulent cases within Bitcoin transactional data. We explore multiple GNN variants, alongside various graph sampling methodologies. To overcome the shortcomings of these sampling methods, we propose a new sampling method BFRON—a hybrid approach mixing Breadth-First Search and Frontier Sampling. Additionally, we introduce an enhanced optimization pipeline and a new metric to improve fraudulent node detection. Evaluation metrics, including Instance Information Gain and Group Distance Ratio, are employed to analyze the challenges of over-smoothing in Graph Neural Networks and the efficacy of diverse graph sampling techniques. Our results show that overall BFRON is the best solution and RGGCN is the best-performing GNN. Moreover, we show that our enhanced pipeline and the usage of graph normalization have important advantages.

A Bitcoin Generator Scam (BGS) is a type of cyberattack in which scammers promise to provide individuals with free cryptocurrencies if they pay a mining fee. Although graph neural networks (GNNs) have been used for detecting other cryptocurrency frauds, the usefulness of these methods for BGS detection has not been studied. In this paper, we carry out extensive experiments to assess the use of both standard machine learning (ML) methods and GNNs to detect Bitcoin transactions associated with activities stemming from Bitcoin Generator Scams. We observe that the over-smoothing problem exists in GNNs designed for BGS detection and show that Random Walk Positional Encoding (RWPE) allows representing long-range interactions between far-away transactions in GNNs without causing over-smoothing. We show that the General, Powerful, Scalable (GPS) Graph Transformer with RWPE outperforms both GNN and ML based state-of-the-art fraud detection methods in Bitcoin Generator Scams. We also analyze the effectiveness of Breadth First Search (BFS) for graph sampling and show that it should not be used as it induces bias toward the subnetwork structure. We propose the Random First Search (RFS) sampling alternative and show that this is a more suitable solution.

Input cost reductions and yield optimization can be done using yield precision maps created by machine learning models to address the increase in food demand predicted by 2050. However, without taking into account the spatial structure of the data, the precision map’s accuracy evaluation assessment runs the risk of being overly optimistic. In the current work, a corn yield prediction study was conducted, and the predictive abilities of two vegetation indices (VIs) and five spectral bands for a single image acquisition date were evaluated. We also examined the impacts of image spatial and spectral resolution on model performance. We used a Canadian smart farm’s yield data, multispectral (MS) and red-green-blue (RGB) imagery captured by unmanned aerial vehicles (UAVs), and we trained deep neural networks (DNN), random forest (RF), and linear regression (LR) models using standard cross-validation and spatial cross-validation approaches. We found that multi-band datasets led to better performance than single-VI datasets. MS imagery led to generally better performance than RGB imagery. High spatial resolution imagery led to better performance than lower spatial resolution imagery. RF was the best performing model while LR was the worst. The choice of RF’s hyperparameters had more of an impact on performance when the number of features was small and less of an impact when the number of features was large or when the input dataset had a lot of spatial structure.

The class imbalance problem is associated with harmful classification bias and presents itself in a wide variety of important applications of supervised machine learning. Measures have been developed to determine the imbalance complexity of datasets with imbalanced classes. The most common such measure is the Imbalance Ratio (IR). It is, however, widely accepted that the complexity of a classification task is the combined result of class imbalance and other factors, such as class overlap. Thus, in order to accurately assess the complexity of a problem, the data complexity measures ought to account for more than the simple IR. In this paper, we demonstrate that IR has a weak correlation with classifier performance in terms of macro averaged recall, gmean score, and precision. Other more complete measures such as the adapted N1 and N3 measures use neighborhood information to assess overlap. These measures show a strong negative correlation with classifier performance, but their reported values were hard to interpret. This motivates a new measure that estimates overlap complexity and returns a value with a clear interpretation. Here we propose such a measure based on the number of minority instances entangled in a Tomek Link. The proposed measure is evaluated on a large selection of synthetic and real datasets and is found to be as good as or better than the best competitors in terms of its negative correlation with respect to mean classifier performance.

Recently, we have witnessed that deep learning-based approaches have been widely applied. Particularly, some applications involve data that are high dimensional, sparse or imbalanced, which are different from those applications with dense data processing, such as image classification and speech recognition, where deep learning-based approaches have been extensively studied. One of the main applications is the user-centric platform that consists of great deal of users, items and user generated tabular data which are quite high-dimensional. The characteristics of such data pose unique challenges to the adoption of deep learning in these applications, including modeling, training, and online serving, etc. More and more communities from both academia and industry have initiated the endeavors to solve these challenges. This workshop will provide a venue for both the research and engineering communities to discuss and formulate the challenges, utilize opportunities, and propose new ideas in the practice and theory of deep learning on high-dimensional, sparse and imbalanced data.

Active learning strategies are used to acquire an enlarged labelled training set that allows the learner to achieve a better performance. To this end, unlabelled instances are carefully selected for labelling by an human expert in order to achieve the best performance with the smallest number of questions to this oracle. Several techniques exist to select the most informative samples within active learning strategies. However, the effectiveness of these methods when applied to problems with imbalanced classes was not studied before. In this paper, we focus on the improvement of instance selection strategies for active learning techniques in the presence of class imbalance. In an imbalanced setting, learning algorithms have difficulties to focus on the minority class due to its under-representation. Still, this is typically the class of interest for the end-user. This mismatch between the classes distribution and the goals of the end user is known as the class imbalance problem. In an active learning setting for class imbalance problems, this becomes an even more challenging issue. We propose two novel active learning algorithms, ALOD and ALOD-RE centered around selecting the most informative samples to be labelled, while considering the selection of possible minority class cases and the generation of synthetic minority class examples to improve the learner performance. To this end, our two proposed solutions combine: active learning procedures, resampling strategies, and anomaly detection methods. Through an extensive set of experiments we show that the incorporation of outlier detection and resampling techniques in the active learning procedure benefits the learners performance on imbalanced domains. The performance advantages of our proposed ALOD and ALOD-RE algorithms are clearly supported by our experimental results.

Malware detection is a well-known problem with severe consequences in terms of damages and financial losses. The typical imbalance of this domain causes serious problems to the learning algorithms that are not able to focus correctly in the scarce malware cases. Although resampling techniques have shown to be effective, their impact has not yet been studied for the particular domain of malware detection. This paper focus on the evaluation of resampling methods to tackle the malware detection problem under a realistic assumption of malware rarity. Several machine learning-based solutions proposed for this domain use non-public data sets and consider different malware prevalence in the data. This makes difficult the comparison of solutions. We use the freely available NSL-KDD data set which has a rarity associated with the malware cases that resembles a more realistic scenario. Our main goal is to assess the potential advantages of applying resampling techniques in this setting as well as their impact when using different standard learning algorithms. We also carry out an extensive analysis of resampling techniques using different parameters which allows to test the best configuration of these techniques. We explore not only a balanced class scenario but also other class configurations that may be more beneficial to improve the predictive performance of the algorithms. We present a systematic study of the effectiveness of existing resampling approaches for tackling the imbalance problem in malware domains. We show that resampling techniques present an advantage and explore their impact in the predictive accuracy of each class. Finally, we also show that different resampling strategies have a different impact in the importance of the features used by the learning algorithms.

The class imbalance problem has been thoroughly studied over the past two decades. More recently, the research community realized that the problem of imbalanced distributions also occurred in other tasks beyond classification. Regression problems are among these newly studied tasks where the problem of imbalanced domains also poses important challenges. Imbalanced regression problems occur in a diversity of real world domains such as meteorological (predicting weather extreme values), financial (extreme stock returns forecasting) or medical (anticipate rare values). In imbalanced regression the end-user preferences are biased towards values of the target variable that are under-represented on the available data. Several pre-processing methods were proposed to address this problem. These methods change the training set to force the learner to focus on the rare cases. However, as far as we know, the relationship between the data intrinsic characteristics and the performance achieved by these methods has not yet been studied for imbalanced regression tasks. In this paper we describe a study of the impact certain data characteristics may have in the results of applying pre-processing methods to imbalanced regression problems. To achieve this goal, we define potentially interesting data characteristics of regression problems. We then conduct our study using a synthetic data repository build for this purpose. We show that all the different characteristics studied have a different behaviour that is related with the level at which the data characteristic is present and the learning algorithm used. The main contributions of our work are: i) to define interesting data characteristics for regression tasks; ii) to create the first repository of imbalanced regression tasks containing 6000 data sets with controlled data characteristics; and iii) to provide insights on the impact of intrinsic data characteristics in the results of pre-processing methods for handling imbalanced regression tasks.

Accounting for misclassification costs is important in many practical applications of machine learning, and cost-sensitive techniques for classification have been studied extensively. Utility-based learning provides a generalization of purely cost-based approaches that considers both costs and benefits, enabling application to domains with complex cost-benefit settings. However, there is little work on utility- or cost-based learning for regression. In this paper, we formally define the problem of utility-based regression and propose a strategy for maximizing the utility of regression models. We verify our findings in a large set of experiments that show the advantage of our proposal in a diverse set of domains, learning algorithms and cost/benefit settings.

Time series forecasting is a challenging task, where the non-stationary characteristics of the data portrays a hard setting for predictive tasks. A common issue is the imbalanced distribution of the target variable, where some intervals are very important to the user but severely underrepresented. Standard regression tools focus on the average behaviour of the data. However, the objective is the opposite in many forecasting tasks involving time series: predicting rare values. A common solution to forecasting tasks with imbalanced data is the use of resampling strategies, which operate on the learning data by changing its distribution in favor of a given bias. The objective of this paper is to provide solutions capable of significantly improving the predictive accuracy of rare cases in forecasting tasks using imbalanced time series data. We extend the application of resampling strategies to the time series context and introduce the concept of temporal and relevance bias in the case selection process of such strategies, presenting new proposals. We evaluate the results of standard regression tools and the use of resampling strategies, with and without bias over 24 time series data sets from 6 different sources. Results show a significant increase in predictive accuracy of rare cases associated with the use of resampling strategies, and the use of biased strategies further increases accuracy over the non-biased strategies.