## 1 Introduction  
The United Nations (UN) has advocated for the Sustainable Development Goals (SDGs), which have since become an indispensable cornerstone for research frameworks within the academic and scientific communities. The Sustainable Development Goals (SDGs) are an all-encompassing collection of seventeen goals, of which SDG 11 addresses the critical challenge of promoting sustainable cities and communities worldwide. Conversely, contemporary trends underscore the increasing demand for secure systems and user privacy. This imperative did not emerge spontaneously; it is rooted in a multitude of documented cases where users suffered adverse consequences due to insecure systems and data breaches. These users encompass a wide spectrum, including corporations, governmental entities, and private citizens [1; 2]. The authors of [2] have documented that between 2018 and 2019, the United States experienced an alarming number of data breaches exceeding 10,000 cases. Notably, among these, 430 incidents were categorized as major data breaches [3]. In the European Union, since May 2018, over 160,000 cases have been officially reported. One particularly noteworthy instance revolves around the activities of Cambridge Analytica [4], which exploited fifty million Facebook profiles associated with American voters. Their objective was to develop an AI system capable of influencing electoral outcomes among targeted demographic groups. The Equifax data breach in 2017 was a significant incident that affected nearly half of the United States' population. This breach compromised over 143 million data records [5; 6]. Similarly, Orvibo, a company specializing in Internet of Things (IoT) management platforms, exposed over 2 billion records of private information, including camera recordings of conversations, on the Internet, without any password or security measures [7]. These incidents, among others, highlight the need for robust security solutions to protect user privacy. Smart city applications often require access to sensitive user information, making it imperative to safeguard this data. Federated learning (FL), also known as \"privacy by design\", is a promising paradigm that preserves user privacy by keeping personal data on local devices, also known as federated clients. Implementing FL can be seen as adding additional security layers to a system. Previous studies have identified the limitations of machine learning in terms of preserving data privacy [8]. Adversaries can potentially derive training data from the gradients of machine learning models. Deep leakage from gradients (DLC) [9] and similar attacks [10; 11] can recover information pixel by pixel for images and token by token for text. However, these attacks still face challenges in consistently converging and uncovering true labels. Alternative research has proposed a method for reconstructing images from the parameter gradients of models by inverting gradients [12] using adversarial attacks. One limitation of this approach is its high computational cost--reconstructing a single image requires 24,000 iterations. [PERSON] et al. [13] conducted a comprehensive study of adversarial threats to distributed machine learning and federated learning. Their findings suggest that FL has several advantages over distributed machine learning. The lack of communication requirements with other clients in FL significantly reduces the risk of privacy breaches. Additionally, distributed machine learning requires more collaboration with other clients or system nodes than FL. It is worth noting that experts in the field have hypothesized that FL systems may offer enhanced security compared to centralized systems. The insights gained from our previous research, as cited in reference [14], establish the foundation for the subsequent analysis presented in this paper. This analysis primarily focuses on identifying potential vulnerabilities and areas where data could be compromised in the event of a security breach.