Federated Learning for Cybersecurity in Edge and Cloud Computing

Purpose: The article explores the integration of federated learning within edge and cloud computing frameworks to address complex cybersecurity challenges. It aims to illustrate how federated learning, by enabling collaborative model training across decentralized devices without data exchange, can serve as an effective mechanism for enhancing cybersecurity defenses. This study investigates the potential of federated learning to improve privacy-preserving data analysis and augment real-time threat detection capabilities in the context of the growing Internet of Things (IoT) ecosystem. Methodology: The research delves into the conceptual framework of federated learning, examining its application in cybersecurity contexts through a detailed literature review and theoretical analysis. It evaluates the benefits and limitations of federated learning in enhancing data privacy and reducing latency in threat detection. Furthermore, the article assesses the technical and security challenges of implementing federated learning, including communication overhead, model aggregation complexities, and vulnerability to model poisoning, through qualitative analysis. Findings: The study finds that federated learning significantly improves privacy-preserving data analysis and enhances real-time threat detection capabilities by keeping data localized while enabling collaborative learning. However, it also identifies key challenges in deploying federated learning strategies, such as the risk of model poisoning and the complexities involved in model aggregation and communication overhead. The research highlights the need for robust mechanisms to address these challenges to fully leverage federated learning in cybersecurity. Unique Contribution to Theory, Policy, and Practice: This article contributes uniquely to the theoretical understanding of federated learning as a cybersecurity measure, offering a comprehensive analysis of its applications, benefits, and limitations within edge and cloud computing environments. Practically, it provides insights for cybersecurity professionals and researchers on integrating federated learning into existing cybersecurity frameworks to enhance data privacy and threat detection. The article recommends further exploration into combining federated learning with other cutting-edge technologies to develop resilient cybersecurity measures. Additionally, it suggests that policymakers should consider the implications of federated learning on data privacy regulations and cybersecurity standards. Through its thorough examination of federated learning's potential and challenges, the article offers valuable recommendations for fortifying cybersecurity frameworks in an increasingly interconnected world.