In this research, we address a critical vulnerability of Federated Learning (FL) in practical applications: their susceptibility to adversarial attacks from malicious participants, which can severely degrade a global model’s performance. We propose MADE-PI, a novel defense mechanism designed to identify and exclude malicious clients early in the training process. Our method introduces a pre-aggregation step where the server evaluates each client update using a Proportional-Integral (PI) score. This score is a unique distance-based metric inspired by the PID controller, where a proportional term P measures the immediate deviation of a client’s update, and an integral term I tracks its behavior over time. By analyzing the distribution of these scores, our technique effectively flags and removes malicious contributions. We validate our approach through an empirical study on three datasets representing various applications of FL, including intelligent transportation, medical image analysis, and handwriting recognition. We concentrate on data poisoning as a highly relevant class of attacks. The results demonstrate that MADE-PI outperforms conventional defenses by providing superior malicious client detection and improving both learning efficiency and final model accuracy.
As collaborative robots (cobots) become available in industry, it is crucial to enable non-coders to program cobots easily. For example, in sorting tasks that may require adjustments over time, workers could easily adjust the cobot’s behavior by utilizing learning from demonstrations (LfD) approaches. This paper presents an LfD approach for enabling a cobot to learn sorting tasks by integrating the Gaussian Mixture Model (GMM), validated in Mujoco for policy training. Quantitative analysis shows that the improved BC-GMM achieves the lowest mean squared error (MSE = 0.00207) and highest R-squared score (R2=0.89), outperforming other methods, such as standard BC, diffusion policy, and implicit behavioral cloning (IBC).
In the era of big data, effective feature selection is critical for improving model performance, reducing computational cost, and enhancing interpretability in high-dimensional datasets. This paper introduces a novel filter-based feature selection algorithm, Class-wise Mean Deviation (CMD), which quantifies the discriminative power of features by measuring the absolute deviation of class-wise means from the global mean. Designed for scalability and real-world application, CMD is computationally efficient and particularly effective in handling the severe class imbalance common in large-scale datasets. We evaluate CMD on two large-scale, highly imbalanced benchmark intrusion detection datasets—CICIDS2017 and CICIDS2019—and compare its performance against established filter methods including Variance Threshold, Pearson Correlation, Mutual Information, and Fisher Score. Experimental results demonstrate that CMD consistently achieves competitive or superior classification performance, even with a minimal number of features. These findings highlight CMD’s potential as a robust and interpretable feature selection technique for cybersecurity and other high-dimensional domains.
Due to its low cost and non-invasive nature, electroencephalography (EEG) is widely used in the diagnosis of neurological disorders and in monitoring cognitive states, such as the detection of Alzheimer’s disease (AD). While deep learning methods have shown promise in AD detection from EEG, signal noise and variability across channels and recording durations remain significant challenges. In this study, we propose EDLAD, an ensemble deep learning approach for Alzheimer’s detection that integrates multiple deep learning models, each specialized in different types of EEG-derived features. EDLAD leverages ensemble learning to improve predictive performance by combining the outputs of multiple models. Specifically, three deep learning models are independently trained: a Transformer-based model and two dense network classifiers. The Transformer-based model, called Conformer, which integrates the convolutional and transformer layers, is designed to process the original EEG signals. In parallel, features in the time and frequency domains are extracted and fed into two separate neural networks to complement the learning process and improve the robustness of the final predictions. Experiments conducted on two EEG datasets demonstrate that the proposed ensemble framework, EDLAD, consistently improves the classification accuracy at the segment and subject levels across all window lengths. It outperforms both the single Conformer model and a meta-learning ensemble approach. Furthermore, EDLAD surpasses feature-level fusion methods that simply concatenate feature representations from the three models.
Quantization plays a central role in digital communication by mapping continuous-valued signals to a finite set of levels with minimal distortion. Beyond mean-square error, mutual information between the channel input and the quantizer output provides a powerful metric for signal recovery. However, finding the quantizer that maximizes the mutual information is NP-complete for non-binary inputs. To that end, while not optimal, thresholding schemes, whether single-threshold or multi-threshold, are widely adopted. In this work, we study the parallel stochastic single-threshold quantizer architecture, provide some information-theoretic insights, and introduce a momentum-accelerated gradient ascent algorithm that efficiently tunes a single decision threshold to maximize the mutual information. We demonstrate convergence improvements over exhaustive search and quantify mutual information gains across binary and non-binary input distributions. We also validate our theoretical framework with simulations on the MNIST dataset, demonstrating that increasing the number of parallel quantization branches, i.e., mutual information, significantly improves classification accuracy, especially when quantization thresholds are learned and training data is limited.
