This study focuses on the important task of optimizing device clustering and assigning them to edge servers, while also implementing data redistribution in hierarchical semi-synchronous federated learning within the realm of advancing edge computing. Our research goal is to increase the performance and scalability of federated learning systems by improving resource allocation and data processing efficiency, which will in turn enhance edge computing frameworks. The current literature does not have thorough methods that can effectively combine model accuracy with optimal device clustering algorithms in hierarchical semi-synchronous federated learning, leading to below-par performance and inefficient use of resources. This difference highlights the need for creative measures that enhance not only model training accuracy but also the grouping of devices as opposed to current methods. The study utilizes a Graph Neural Network (GNN) to group IoT devices according to their hardware features and local datasets, then applies the K-means algorithm to create efficient device clusters. After that, Hybrid Data Redistribution is used to equalize local datasets in each cluster, and Proximal Policy resource allocation optimization algorithm is implemented to allocate devices to edge servers according to bandwidth usage, and energy consumption based on real-time updates, ultimately enabling hierarchical semi-synchronous federated learning to improve model training. The results show a 15% increase in clustering metrics compared to current algorithms, showcasing how our method improves device assignment and data redistribution in hierarchical semi-synchronous federated learning, addressing issues in model accuracy and resource optimization.
Hadi Farajvand, Nahideh Derakhshanfard, Abbas Mirzaei et al.· Scientific Reports· 0 citations
Emotion detection from textual data is a key challenge in natural language processing (NLP), playing an important role in applications such as sentiment analysis, human-computer interaction, and psychological evaluation. Growing utilization of social networks and online portals leads to the creation of a huge amount of reviews and ratings. Analyzing users’ and customers’ reviews and opinions are so important for governments and businesses. While recent advances have primarily leveraged transformer-based architectures for this task, we propose a novel approach by employing Deep Operator Networks (DeepONets), originally designed for learning operators in scientific computing, to model the mapping between textual representations and emotional states. In this study, we extract high-dimensional semantic embeddings from text using a pre-trained sentence transformer model and feed these embeddings into a DeepONet architecture for emotion classification. The primary contribution of this work lies in architectural innovation. To the best of our knowledge, this is the first application of DeepONet to textual emotion recognition, introducing a fundamentally different perspective on function approximation in language understanding tasks. Results obtained from experiments on benchmark emotion-labeled datasets indicate that our proposed model attains performance and results comparable to related baselines, with notable generalization capabilities across emotion categories. To ensure a fair and comprehensive evaluation, we assessed the performance of the proposed model using widely adopted classification metrics, including accuracy, precision, recall, and F1-score. We used two datasets and the results were around 80 percent on one dataset for all named metrics, and around 88 percent on the other. The findings suggest that DeepONet can serve as a robust alternative framework for capturing and modeling complex relationships in natural language processing tasks and opens new avenues for operator-based learning in text analysis.
Baharak Ahmadipoor, Abbas Mirzaei, Babak Nouri-Moghaddam et al.· Journal of Intelligent &...· 0 citations
EffABPMN2MV represents a theoretically grounded and empirically validated contribution to BPM, collaborative software design, and blockchain-enabled information systems.
Masoud Rezaei, Abbas Mirzaei, Babak Nouri-Moghaddam et al.· Journal of Resource Manageme...· 0 citations