… Federated learning (FL) is an ML approach that trains the model using multiple independent
sessions. In contrast, the classical centralized ML model, where local datasets are …

… For future work, we plan to integrate the proposed approach using Federated Learning (FL)
to improve security and privacy issues. In addition, investigate the efficiency of the proposed …

… In the study conducted earlier [14], the FIDChain IDS was suggested by employing the
weightless ANN in a federated learning (FL) manner. This was done so to confirm the …

… Harris Hawks have exceptional sight for locating and monitoring their prey but frequently
struggle to locate it. Therefore, the hawks … learning and deep learning algorithms with the Harris …

… learning repository and designed a deep learning model with principal component analysis
(PCA) for dimensionality reduction, and to extract the most important features, Harris hawks …

… of Harris Hawks optimization. This chapter proposes a new hybridization strategy, namely,
hybrid Harris Hawks … (exploitation) search skill of the Harris Hawks’ optimization to achieve the …

… This paper combines the status quo of federated learning, stratifies the federated learning …
horizontal federated learning and vertical federated learning. In horizontal federated learning, …

… employing multi-level thresholding using the Harris Hawks Optimization (HHO) algorithm [32…
to the Harris Hawks population in each generation. HHO is inspired by how Harris Hawks …

… One of the metaheuristic algorithms is the Harris Hawks Optimizer (HHO). It is a swarm-inspired
algorithm used to find the best solutions in a search area by mimicking the cooperative …

… To address this, we propose a Multi-objective Harris Hawks Optimization (HHO)-based task
scheduling algorithm (MoHHOTS) for cloud-fog computing networks, aiming to optimize task …