Sequential change point detection for multivariate autocorrelated data is a very common problem in practice. However, when the sensing resources are limited, only a subset of variables from the multivariate system can be observed at each sensing time point. This raises the problem of partially observable multi-sensor sequential change point detection. For it, we propose a detection scheme called adaptive upper confidence region with state space model (AUCRSS). It models multivariate time series via a state space model (SSM), and uses an adaptive sampling policy for efficient change point detection and localization. A partially-observable Kalman filter algorithm is developed for online inference of SSM, and accordingly, a change point detection scheme based on a generalized likelihood ratio test is developed. How its detection power relates to the adaptive sampling strategy is analyzed. Meanwhile, by treating the detection power as a reward, its connection with the online combinatorial multi-armed bandit (CMAB) problem is formulated and an adaptive upper confidence region algorithm is proposed for adaptive sampling policy design. Theoretical analysis of the asymptotic average detection delay is performed, and thorough numerical studies with synthetic data and real-world data are conducted to demonstrate the effectiveness of our method.

The Internet of Federated Things (IoFT) represents a network of interconnected systems with federated learning as the backbone, facilitating collaborative knowledge acquisition while ensuring data privacy for individual systems. The wide adoption of IoFT, however, is hindered by security concerns, particularly the susceptibility of federated learning networks to adversarial attacks. In this paper, we propose an effective non-parametric approach FedRR, which leverages the low-rank features of the transmitted parameter updates generated by federated learning to address the adversarial attack problem. Besides, our proposed method is capable of accurately detecting adversarial clients and controlling the false alarm rate under the scenario with no attack occurring. Experiments based on digit recognition using the MNIST datasets validated the advantages of our approach.

Classification applications are typically associated with misclassification costs and benefits as a result of incorrect and correct classification, respectively. Many studies have focused on cost-sensitive classification approaches [7, 8, 9, 10, 11, 12] in an effort to reduce the costs of misclassification. We illustrate the concept of imbalanced misclassification costs using the current and real-world example of classifying COVID-19 patients. Incorrectly classifying an ill patient as healthy may put this patient's life at risk as well as others by allowing the ill person to circulate among healthy persons and infect them (an intangible cost, usually determined by the judicial system). Classifying a healthy individual as a COVID-19 patient, on the other hand, may lead to unnecessary treatment, misuse of medical resources and cause unnecessary financial hardship to the individual and the general economy. Many studies have applied cost-sensitive approaches to handling imbalanced classification problems [13, 14] where the decision maker is interested in detecting the positive cases. There are four main approaches for making a classifier cost-sensitive: (i) changing the distribution of classes using over-and under-sampling within the training data set (i.e., preprocessing of the training data) to reduce misclassification costs [7, 8], denoted hereafter approach A1; (ii) changing the data set according to the misclassified samples of the cost-insensitive classifiers and their error costs (post-processing the training data) using a boosting approach in ensemble learning methods [12, 15], denoted hereafter approach A2; (iii) incorporating meta-learning methods on outputs of cost-insensitive learners using threshold driven techniques in favor of utilizing the probability estimations for the classes [7, 8, 16, 17], hereafter denoted A3; (iv) directly incorporating cost-sensitive capabilities into a learning algorithm, i.e., an algorithm-level solution that adapts existing learning methods so they are biased towards classes with high misclassification costs, usually presented by minority classes [8, 18].