Cyber Resilience: Federated Learning and Adaptive AI Through BCI and Neurotechnological Synergy explores the transformative role of artificial intelligence, federated learning, brain–computer interfaces (BCIs), and neurotechnology in strengthening modern cybersecurity. As cyber threats become increasingly sophisticated, traditional security approaches are no longer sufficient to protect critical systems, sensitive data, and digital infrastructure. This book presents innovative, privacy-preserving strategies that combine decentralized AI, adaptive learning, and neurotechnological advances to build secure and resilient digital ecosystems. The book examines how Federated Learning enables collaborative AI model development without sharing sensitive data, while Adaptive AI continuously learns from evolving environments to detect, predict, and respond to emerging cyber threats. It also highlights the growing role of BCIs and neurotechnology in enhancing authentication, decision-making, cognitive cybersecurity, and human–AI collaboration. Covering both theoretical foundations and practical applications, the book discusses topics such as explainable AI, adversarial learning, secure edge and cloud computing, blockchain-enabled trust, zero-trust security, and ethical AI. Through real-world case studies from healthcare, finance, smart cities, defense, and Industry 5.0, it demonstrates how these technologies can enhance cyber resilience. Designed for researchers, practitioners, students, and policymakers, this book provides valuable insights into developing intelligent, trustworthy, and future-ready cybersecurity solutions.
