Paper Details

Name: Anupam Sharma, Arbaz Raza, Kunal Chauhan, Simranpreet Kaur, Udit Dagar & Digvijay Singh Shekhawat
Country: India

Year: 2026
Volume: Volume No: 13, January, Year: 2026 (Special Issue)
Page Number: 267-275
DOI: https://doi.org/10.5281/zenodo.18978263

ABSTRACT—
The review paper analyzes cybersecurity through examining how federated learning works with distributed intrusion detection systems for implementation and performance effectiveness. The increasing danger from cyberattacks forces traditional intrusion detection systems to struggle in their ability to respond to present-day cybersecurity threats. Federated learning presents itself as a solution to improve distributed network detection through decentralized machine learning models. The abstract presents a thorough research on federated learning approaches together with their intrusion detection system applications that boost detection precision and operational speed. The paper explores both the advantages and difficulties implied by federated learning systems while discussing security-related issues along with privacy protection needs and network traffic management problems and model distribution mechanisms. The study uses case studies and
experimental results to illustrate the functional advantages that result from federated learning implementation across different network configurations. The paper provides essential research and practical guidance about present-day distributed intrusion detection breakthroughs to scholars and security experts and technical professionals. Based on existing research synthesis and important discoveries we intend to steer upcoming research directions for building improved cybersecurity solutions suited to distributed computing systems.

Keywords— Federated learning, cybersecurity, intrusion detection, distributed networks, machine learning, privacy preservation, model synchronization, communication overhead, network security, adaptive cybersecurity.