## 2 Related Work  
In previous studies, diverse methods have been investigated to address security and privacy concerns within the realm of smart cities and the Internet of Things (IoT). These methods encompass the deployment of deep-learning-based intrusion detection systems, blockchain technology, and anomaly detection techniques, among others. The overarching objective of these approaches is to comprehensively fortify overall security measures. Additionally, several novel computational frameworks and architectures have been proposed to augment privacy protection. The emergence of edge GPU devices, equipped with substantial computational capabilities facilitated by GPUs, has given prominence to the concept of edge AI. Consequently, the notion of training AI models at the edge has garnered significance and viability. In the realm of smart city applications, [PERSON] et al. [37] introduced DeepBlockScheme, a solution that combines deep learning with blockchain technology to enhance security. To illustrate its practicality, the authors conducted a case study within the domain of vehicle manufacturing. Another study by [PERSON] et al. [38] employed blockchain and machine learning to enhance privacy and security in IoT-driven smart cities. In this context, a blockchain module ensures secure IoT data transmission. Principal component analysis (PCA) is applied to process raw IoT data into a new representation, and an intrusion detection system, the gradient boosting anomaly detector (GBAD), flags unauthorized access attempts. Despite the data integrity benefits offered by blockchain, its implementation introduces computational complexity, particularly in resource-constrained environments [27]. It is noteworthy that in contemporary blockchain systems like Ethereum, data stored on blockchains is commonly publicly accessible by default, posing potential privacy concerns for individuals [33]. Prior studies have introduced various frameworks and architectures aimed at supporting IoT technologies in the context of smart cities. In [39], the authors leverage blockchain and software-defined networking to ensure secure data communication, privacy, and reduced computational costs. They incorporate deep learning within the cloud architecture, enhancing smart industry production with valuable analysis. Another approach is presented in [20], where the authors utilize mobile edge computing (MEC) and the Stackelberg principle-based game theory implemented through the alternating direction method of multipliers (ADMM) within an IoT environment. This method efficiently schedules tasks for distributed devices, resulting in rapid algorithm convergence. Addressing challenges in multimedia data management, [16] introduces the Short Supply Circuit Internet of Things (SSCIoT). This research primarily focuses on tackling data processing challenges when cloud access is limited or unavailable. The architecture emphasizes the utilization of mobile edge computing and fog computing over cloud computing to deliver quicker responses to users. Numerous researchers have explored the integration of federated learning (FL) within the IoT and smart city domains, deviating from conventional approaches involving data transfer to multiple points. The foundational principle of FL revolves around maintaining data on the client side, as discussed earlier, aiming to fortify system security and privacy. In our prior research [40], we introduced a federated trustworthy AI (FTAI) architecture tailored to meet seven key requirements of trustworthy AI (TAI) outlined by the European Union [41]. Specifically, in this paper, the proposed method delves into TAI requirements two and three, focusing on robustness, safety, privacy, and data governance. In a comprehensive survey [42], researchers explored FL's implementation for IoT, covering aspects such as data exchange, offloading, caching, attack detection, location services, mobile crowdsensing, privacy, and security.