Dynamic Circular Network-Based Federated Dual-View Learning for Multivariate Time Series Anomaly Detection

Multivariate time-series data exhibit intricate correlations in both temporal and spatial dimensions. However, existing network architectures often overlook dependencies in the spatial dimension and struggle to strike a balance between long-term and short-term patterns when extracting features from the data. Furthermore, industries within the business community are hesitant to share their raw data, which hinders anomaly prediction accuracy and detection performance. To address these challenges, the authors propose a dynamic circular network-based federated dual-view learning approach. Experimental results from four open-source datasets demonstrate that the method outperforms existing methods in terms of accuracy, recall, and F1_score for anomaly detection

Lightweight Transformer in Federated Setting for Human Activity Recognition

Human activity recognition (HAR) is a machine learning task with applications in many domains including health care, but it has proven a challenging research problem. In health care, it is used mainly as an assistive technology for elder care, often used together with other related technologies such as the Internet of Things (IoT) because HAR can be achieved with the help of IoT devices such as smartphones, wearables, environmental and on-body sensors. Deep neural network techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been used for HAR, both in centralized and federated settings. However, these techniques have certain limitations: RNNs cannot be easily parallelized, CNNs have the limitation of sequence length, and both are computationally expensive. Moreover, the centralized approach has privacy concerns when facing sensitive applications such as healthcare. In this paper, to address some of the existing challenges facing HAR, we present a novel one-patch transformer based on inertial sensors that can combine the advantages of RNNs and CNNs without their major limitations. We designed a testbed to collect real-time human activity data and used the data to train and test the proposed transformer-based HAR classifier. We also propose TransFed: a federated learning-based HAR classifier using the proposed transformer to address privacy concerns. The experimental results showed that the proposed solution outperformed the state-of-the-art HAR classifiers based on CNNs and RNNs, in both federated and centralized settings. Moreover, the proposed HAR classifier is computationally inexpensive as it uses much fewer parameters than existing CNN/RNN-based classifiers.Comment: An updated version of this paper is coming soo

Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented