Analyzing big data using federated learning (FL) requires distributing the data to different clients to process locally and sending the model parameters to the global server for aggregation. In a real scenario, big data distribution is nonindependent and identically distributed (non-IID). Aggregating client updates is an important task in federated learning. Federated averaging (FedAvg) is the simplest and most popularly used method in FL. However, it is unable to handle heterogeneous (non-IID) data. To address this, in this paper we propose a Client Specific Dynamic Aggregation (CSDA) strategy focusing on dynamic aggregation based on client-specific metrics, aiming to improve the robustness and performance of the global model. Each client’s contribution is weighted by the quality and performance of their local updates, enhancing the overall federated learning process. Extensive experimentation has demonstrated that the proposed CSDA strategy, in conjunction with advanced comparison techniques, has the potential to greatly enhance the accuracy of each client across three real-world datasets.

Accurate energy consumption forecasting is becoming increasingly important due to rising global energy demands driven by economic development and population growth. Traditional forecasting models often overlook the impact of contextual factors, such as weather conditions and occupancy trends, which are essential for precise predictions. In this study, we propose a hybrid context-aware simulated scenario generation (CA-SSG) approach that integrates context space theory (CST) with deep learning techniques. This method leverages key contextual features to generate synthetic energy consumption data that more accurately mimics real-world patterns. Using the ASHRAE Great Energy Predictor III dataset, which includes diverse building types across various climates, we demonstrate the effectiveness of CA-SSG. The results show significant improvements in model performance, with reductions in Kullback-Leibler divergence (5%), increases in Pearson Correlation Coefficient (5%), and decreases in computation time compared to traditional approaches. These findings highlight the advantages of contextually enriched generative models for developing smarter energy management systems, enabling more accurate energy forecasting, and supporting strategic planning for energy consumption.

Online learning has yielded numerous advantages, notably enhanced accessibility and resource efficiency, which have played a vital role in sustaining educational continuity amidst unprecedented challenges, such as the COVID-19 pandemic. Despite the various benefits and opportunities provided by online learning, many challenges need to be addressed. For instance, the virtual learning environment may introduce potential barriers to effective communication and interaction. It has been established that genuine student engagement is pivotal for effective learning, surpassing the mere availability of high-quality educational materials. Utilizing deep learning (DL) architectures, we harness artificial intelligence (AI) to propose a multimodal approach for assessing and evaluating student engagement in online learning environments. Our results are promising, showcasing the potential impact of AI in enhancing online learning experiences for both students and educators. Additionally, we present an emotion classifier that outperforms the widely recognized DeepFace emotion recognition model on the test set, increasing accuracy from 54% to 72%. We aspire to stimulate further research in this direction, as the ongoing shift towards digital and online learning necessitates innovative solutions to ensure that educational outcomes remain robust and equitable for all learners.

Environment perception through deep computation in unstructured environments is important for the construction of autonomous navigation systems. Most research focuses on navigation in structured scenes, including indoor mobility and driving along roads, while neglecting to consider unstructured environments, which often contain diverse heights and distributions. In addition, existing depth estimation algorithms based on deep learning often need to complete training under the supervision of Ground truth, and GT data with a large number of labels are not always easy to obtain. To tackle this issue, this paper proposes an unsupervised stereo depth estimation method for processing UAV navigation images in an unstructured environment. The method contains a primitive U-shaped CNN network architecture for processing such scenes. The feature extraction layer of the network is based on the YOLOv3 residual structure, and additional attention modules help the network enhance its ability to perceive image features. Finally, depth estimation experiments on the unstructured environments dataset Mid-Air further demonstrate the effectiveness and reliability of the proposed method.