Gartner predicted that 75% of the enterprise data will be processed at the edge instead of cloud-based servers by 2025 [8]. The limited computational, storage, and power capabilities of typical edge devices make them vulnerable to unauthorized access by adversaries and restrict the implementation of robust security measures. Although the hierarchical EC architecture improves data security by distributing it across multiple nodes, it also creates opportunities for hackers by exposing the communication links between edge devices and the cloud [9]. EC leverages various technologies to construct its network, creating opportunities for multiple types of attacks. Traditional data security and privacy techniques, originally designed for centralized computational and storage systems of cloud computing, might not meet the unique requirements of a distributed edge computing (EC) ecosystem [10; 11]. The synergy between EC and AI can result in immediate data processing and decision-making at the data source itself, further reducing delay and bandwidth requirements [12; 13]. Artificial Intelligence (AI), machine learning (ML), data mining, and deep learning techniques are utilized for static and dynamic malware analysis, including anomaly detection. AI enhances the ability to detect, predict, and respond to threats effectively. ML, a subset of AI, is used to create and refine security models capable of understanding and adapting to new conditions [14; 15]. An effective learning algorithm can leverage a trained model derived from labeled data to identify emerging security and privacy risks [16]. Traditional machine learning (ML) technologies rely on centralized cloud servers for model training, which raises concerns about data security and often faces resistance from data owners. Federated Learning (FL), an encrypted and distributed ML approach, enables multiple parties to collaborate on developing a model without exposing their raw data [17; 18]. Blockchains offer solutions for FL-based intelligent edge computing (EC) as a ledger technology, leveraging its unique features such as decentralization, immutability, and traceability [19]. However, the distributed nature of blockchain may not be ideal for the EC paradigm. The massive volume of data increases the cost of managing blockchain nodes and storing data. Furthermore, large data volumes generate massive blocks, causing unacceptable verification and propagation delays in an EC environment. Additionally, duplicating IoT data across all nodes leads to inefficient use of storage resources [20]. Physical Unclonable Functions (PUFs) are cryptographic constructs that exploit the inherent physical variations in electronic devices to generate unique outputs. These outputs act as device-specific fingerprints for authentication, key generation, and other security applications [21]. PUFs can be integrated with edge devices, either as a stand-alone Application-Specific Integrated Circuit (ASIC), a subsection of a System-on-Chip (SoC), or within a Field-Programmable Gate Array (FPGA). Reconfigurable architectures, such as FPGAs, are the preferred choice for PUF implementation due to their flexibility, rapid development cycles, and optimal balance between high computational performance and energy efficiency [22; 23; 24]. Further ML with PUFs will improve data security in EC by enhancing device authentication, cryptographic key generation, and anomaly detection. ML optimizes PUF performance by improving noise immunity, adapting to available resources, increasing fault tolerance, enabling dynamic authentication, lightweight cryptographic solutions, and secure IoT device management [25; 26; 21]. This study introduces a unified authentication framework built around an Arbiter PUF (APUF) and its corresponding model to identify security threats. We further propose training the device-specific PUF model using ML algorithms and deploying these modelsacross the edge nodes.