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Edge computing has become a cornerstone of modern real-time processing and Internet-of-Things (IoT) ecosystems, enabling low-latency computation by bringing processing power closer to users. However, this proximity introduces critical cybersecurity challenges, demanding robust and innovative security mechanisms. Collaborative edge computing leverages distributed processing through resource sharing and load balancing across multiple edge nodes, enabling efficient task execution with minimal latency. This emerging paradigm is particularly transformative for resource-constrained environments, such as smart villages, where factors like energy consumption, computational cost, and latency remain critical constraints. Fortified-edge research focuses on the cybersecurity of distributed edge systems, proposing advanced device authentication and authorization protocols for edge data centers using Physical Unclonable Function (PUF). The efficient authentication monitoring and attack detection system ensures safety against diverse external cyber threats. Addressing PUF reliability, the research introduces a comprehensive machine learning-based methodology for error detection and correction enhancing authentication accuracy and system robustness. The multi-layered security framework combines resilient hardware and intelligent software to ensure seamless and secure operations across diverse edge platforms. A federated learning framework is employed to suit the distributed nature of edge computing, enabling decentralized training and deployment of robust security mechanisms. By integrating cutting-edge technologies—such as Hardware-Assisted Security (HAS), Security-by-Design (SbD), and machine learning—this research develops secure, scalable, and sustainable solutions. In summary, Fortified-Edge research provides a critical foundation for advancing secure edge computing, delivering a scalable, distributed, and resilient cybersecurity architecture that meets the demands of the rapidly evolving edge landscape.