Federated Learning-Based Security Analytics Education System
Yeluripati Lalitha Kameswari · 2025
Cybersecurity education is crucial for preparing students to navigate the ever-evolving landscape of digital threats. However, traditional teaching methods often fall short in providing practical, hands-on experiences, and are hindered by challenges related to data privacy and security. This chapter introduces a pioneering approach to address these concerns by integrating Federated Learning (FL) into a Security Analytics Education System. The system fosters a collaborative and secure learning environment, allowing students to actively contribute to model training without compromising the confidentiality of sensitive data. Federated Learning is presented as a decentralized machine learning paradigm designed to facilitate model training across distributed devices or servers. Its inherent capability to operate without the need for centralized data sharing becomes a cornerstone for addressing privacy concerns. The educational context is elucidated, emphasizing the significance of hands-on experiences in security analytics. Traditional teaching methods are scrutinized for their limitations in effectively addressing data privacy and security concerns. The chapter details the seamless integration of Federated Learning into the security analytics education system. Each student's device or server becomes a federated node, participating in collaborative model training without direct sharing of raw security data. Mechanisms ensuring privacy during the model training process are explored. Encryption techniques and secure aggregation methods are highlighted, underlining their role in safeguarding sensitive information. The collaborative nature of Federated Learning is articulated, illustrating how students actively contribute to model improvement. The framework encourages a community of practice, fostering knowledge sharing within a secure environment. Benefits arising from the incorporation of Federated Learning into security analytics education are detailed. Improved model accuracy, real-world applicability, and the development of practical skills emerge as primary advantages. Additional security measures embedded in the system to protect against potential threats are discussed. The system's compliance with data protection regulations and ethical considerations is outlined, ensuring the utmost integrity and security. Attention is devoted to ensuring a seamless and user-friendly experience for both educators and students. Considerations for accessibility in diverse learning environments are meticulously addressed. The chapter explores the scalability and adaptability of the system to different educational settings and cybersecurity curricula. Considerations for future expansion and integration into various learning environments are underscored.