# Visual Data and Pattern Analysis for Smart Education:
## 4 Proposed Methods
### Federated Learning-Based Authentication  
The study [43] significantly expands the discussion on data privacy challenges and personalised education solutions, contributing to a richer literature review. It explores a low-latency edge computation offloading strategy for trust evaluation in finance-level Artificial Intelligence of Things (AIoT) environments. The goal is to minimise latency in data processing and decision-making by relocating these processes to edge devices closer to data sources. This approach aims to improve the speed and reliability of AI operations, thus enhancing the efficiency of technology-driven environments. Similarly, the implementation of federated learning-based authentication in e-learning enhances data privacy and security. This approach allows for the decentralised training of machine learning models across numerous devices without necessitating the sharing of sensitive data, thereby preserving user privacy [44]. It acts as a safeguard against potential data breaches by decentralising data storage and ensuring adherence to privacy regulations. Moreover, federated learning enables the delivery of personalised learning experiences. It optimises bandwidth usage, establishing it as a bandwidth-efficient methodology. By integrating these advanced techniques, the paper underlines the importance of adopting innovative solutions to overcome the prevalent challenges in e-learning platforms. Federated learning, in particular, plays a pivotal role in maintaining the confidentiality and integrity of data, while facilitating a customised educational experience for learners. This synergy between cutting-edge AIoT strategies and federated learning underscores the evolving landscape of secure, personalised, and efficient e-learning environments. To mitigate malicious activities in the adaptive e-learning environment, a robust and privacy-preserving authentication scheme has been developed for both course instructors (CIs) and students, utilising Federated Learning (FL) technology. This scheme involves CIs, students, and cloud servers (CS). The authentication process begins with the registration initiated by CIs and students, who send their requests to the CS. The CS comprises two centralised sub-servers, the Student Server (SS) and the Course Instructor Server (CIS), where students and CIs, respectively, register their credentials. The credentials, which include ID, password, face data, voice data, and keystroke dynamics, are treated as local models. Subsequently, the sub-servers create the global model using a Cycle General Adversarial Network (CGAN). CGAN, consisting of generators (\(\delta\)) and discriminators (\(\epsilon\)) for each domain (features from students and course instructors), aims to translate characteristics from one domain to another. Generators \(\delta_{1}\) and \(\delta_{2}\): convert characteristics between domains. \(\delta_{1}\) transforms SS into CIs, while \(\delta_{2}\) transforms CIs into SS. \[\delta_{1}(\alpha) \rightarrow (\beta) \tag{1}\] \[\delta_{2}(\beta) \rightarrow (\alpha) \tag{2}\]  
_Discriminator \(\epsilon_{1}\) and \(\epsilon_{2}\)_: evaluate the resemblance of generated features to real features in their respective domains. For \(\epsilon_{1}\) (Discriminating SS)  
\[\epsilon_{1}(\beta) \rightarrow [0,1] \tag{3}\]  
For \(\epsilon_{2}\) (Discriminating CIs)  
\[\epsilon_{2}(\alpha) \rightarrow [0,1] \tag{4}\]  
_Cycle consistency loss_: ensures that a feature translated from one domain to another and back again should resemble the original feature, preserving information. We will refer to the translated features as \(\alpha^{\prime}\) (SS) and \(\beta^{\prime}\) (CIs).