Federated Learning (FL) has emerged as a promising distributed machine learning approach that addresses confidentiality and integrity concerns in various sectors, including Internet of Things (IoT), healthcare, finance, and cybersecurity. In order to improve privacy protection and detection accuracy in decentralized systems, this study investigates the incorporation of FL into Intrusion Detection Systems (IDS). FL is especially useful in situations where data security and privacy are crucial because it allows for the cooperative training of models without centralizing sensitive data. We examine many FL-based IDS solutions across several domains, emphasizing how well they mitigate data breaches, maintain confidentiality, and enhance intrusion detection capabilities. The use of Generative Adversarial Networks (GANs), artificial immune systems, and hybrid deep learning techniques to maximize IDS performance are among the current developments in FL methodology that are covered in the paper. We also look at issues like the requirement for effective aggregation procedures and non-independent and identically distributed (non-IID) data. Finally, we outline future directions and open research topics to improve the scalability, resilience, and effectiveness of FL-based IDS solutions in practical applications.

The growing importance of data security in modern information systems extends beyond the preventing malicious software and includes the critical topic of data privacy. Centralized data processing in traditional machine learning methods presents significant challenges, including greater risk of data breaches and attacks on centralized systems. This study addresses the critical issue of maintaining data privacy while obtaining effective malware detection and classification. The motivation stems from the growing requirement for robust and privacy-preserving machine learning methodologies in response to rising threats to centralized data systems. Federated learning offers a novel solution that eliminates the requirement for centralized data collecting while preserving privacy. In this paper, we investigate the performance of federated learning-based models and compare them classic non-federated approaches. Using the CIC-MalMem-2022 dataset, we built 22 models with feedforward neural networks and long short-term memory methods, including four non-federated models. The results show that federated learning performed outstanding performance with an accuracy of 0.999 in binary classification and 0.845 in multiclass classification, despite different numbers of users. This study contributes significantly to understanding the practical implementation and impact of federated learning. By examining the impact of various factors on classification performance, we highlight the potential of federated learning as a privacy-preserving alternative to centralized machine learning methods, filling a major gap in the field of secure data processing.

We introduce two subclasses of biunivalent functions and find estimates on the coefficients and for functions in these new subclasses. Also, consequences of the results are pointed out.

In this current study, we aim to give some results for third-order differential subordination and superordination for analytic functions in $U=\left\{z\in ℂ:\left|z\right|<1\right\}$involving the generalized operator $I_{\alpha ,\beta }^{j}f$. The results are derived by investigating relevant classes of admissible functions. Some new results on differential subordination and superordination with some sandwich theorems are obtained. Moreover, several particular cases are also noted. The properties and results of the differential subordination are symmetry to the properties of the differential superordination to form the sandwich theorems.