Particle Swarm Optimization (PSO) stands as a cornerstone among population-based swarm intelligence algorithms, serving as a versatile tool to tackle diverse scientific and engineering optimization challenges due to its straightforward implementation and promising optimization capabilities. Nonetheless, PSO has its limitations, notably its propensity for slow convergence. Traditionally, PSO operates by guiding swarms through positions determined by their initial velocities and acceleration components, encompassing cognitive and social information. In pursuit of expedited convergence, we introduce a novel approach: the Cognitive and Social Information-Based Hyperbolic Tangent Particle Swarm Optimization (HT-PSO) algorithm. This innovation draws inspiration from the activation functions employed in neural networks, with the singular aim of accelerating convergence. To combat the issue of slow convergence, we reengineer the cognitive and social acceleration coefficients of the PSO algorithm, leveraging the power of the hyperbolic tangent function. This strategic adjustment fosters a dynamic balance between exploration and exploitation, unleashing PSO's full potential. Our experimental trials encompass thirteen benchmark functions spanning unimodal and multimodal landscapes. Besides that, the proposed algorithm is also applied to different UAV path planning scenarios, underscoring its real-world relevance. The outcomes underscore the prowess of HT-PSO, showcasing significantly better convergence rates compared to the state-of-the-art.

Cognitive radio (CR) is considered as a key technology to overcome the problem of spectrum scarcity for wireless applications by the researchers. Efficient spectrum utilization is the most important and core purpose of CR systems after successful spectrum sensing. In this paper, different cases are investigated in which secondary users (SUs) can transmit their data after declaring the primary user (PU) is absent as a result of spectrum sensing. SU’s transmission in some of these cases may lead to interference in PU’s communication, which may cause inefficient spectrum utilization. In this paper, an optimization problem is formulated to optimize the sensing time that maximizes the spectrum utilization efficiency (SUE) under the constraints of high target detection probability and low probability of interference to PU. A trade-off between sensing time and spectrum utilization efficiency is shown in a given time frame for all possible cases. Theoretical and simulation results of proposed system show good match to each others. Finally proposed system is compared with conventional systems and 45% better performance in terms of optimal sensing time is achieved with maximum SUE.

We propose a novel cluster based cooperative spectrum sensing algorithm to save the wastage of energy, in which clusters are formed using fuzzy c-means (FCM) clustering and a cluster head (CH) is selected based on a sensor’s location within each cluster, its location with respect to fusion center (FC), its signal-to-noise ratio (SNR) and its residual energy. The sensing information of a single sensor is not reliable enough due to shadowing and fading. To overcome these issues, cooperative spectrum sensing schemes were proposed to take advantage of spatial diversity. For cooperative spectrum sensing, all sensors sense the spectrum and report the sensed energy to FC for the final decision. However, it increases the energy consumption of the network when a large number of sensors need to cooperate; in addition to that, the efficiency of the network is also reduced. The proposed algorithm makes the cluster and selects the CHs such that very little amount of network energy is consumed and the highest efficiency of the network is achieved. Using the proposed algorithm maximum probability of detection under an imperfect channel is accomplished with minimum energy consumption as compared to conventional clustering schemes.

Explainable Artificial Intelligence (XAI) is a well-established and dynamic field defined by an active research community that has developed numerous effective methods for explaining and interpreting the predictions of advanced machine learning models, including deep neural networks. Clustered Federated Learning (CFL) mitigates the difficulties posed by heterogeneous clients in traditional federated learning by categorizing related clients according to data characteristics, facilitating more tailored model updates, and improving overall learning efficiency. This paper introduces Explainable Clustered Federated Learning (XCFL), which adds explainability to clustered federated learning. Our method improves performance and explainability by selecting features, clustering clients, training local clients, and analyzing contributions using SHAP values. By incorporating feature-level contributions into cluster and global aggregation, XCFL ensures a more transparent and data-driven model update process. Weighted aggregation by feature contributions improves consumer diversity and decision transparency. Our results show that XCFL outperforms FedAvg and other clustering methods. Our feature-based explainability strategy improves model performance and explains how features affect clustering and model adjustments. XCFL’s improved accuracy and explainability make it a promising solution for heterogeneous and distributed learning environments.

Federated learning is an approach of training the global model on the server by utilizing the personal data of the end users while data privacy is preserved. The users, referred to as clients, are responsible for performing local training using their respective datasets. Once trained, the clients forward their local models to the server, where the models are aggregated to update the global model. Practically, the datasets of clients have different classes of labels regardless of the number of samples. In other words, the data is non-independent and identically distributed (non-iid) among clients in terms of classes of labels, which creates heterogeneity among them. Hence, the local model weights updated by clients result in a broad variation due to heterogeneity among their local datasets. Thus, the process of aggregating the diversified local models of clients has a valuable impact in performance of global training. When the server aggregates the local models by calculating weighted average based solely on the number of samples available at the clients, the aggregation process may misguide global training process. To address this issue, our paper proposes a novel reweighting method called FedCLS that performs based on the volume and variance of local datasets among clients. By taking into account the heterogeneity of data for aggregation in federated learning, the proposed method aims to achieve the minimum global point. The simulation results show that the proposed method achieves 28% performance improvement compared to the conventional federated learning methods.