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Massive volumes of data are collected, processed, and analyzed in real time at a critical juncture because of the rapid growth of Internet of Things (IoT) technology and the widespread usage of cloud computing. Fog computing allows data processing and storage to be done more efficiently near to the site of data generation. Through local data management, latency and bandwidth consumption are decreased, facilitating analytics and decision-making in real-time. IoT systems’ scalability, performance, and reliability are improved by fog computing, which shifts processing power to the network's edge. Complicating the security picture is the distributed nature of cloud computing, which positions processing and storage nearer to the many data sources. Because they often lack strong security, IoT devices are vulnerable to a variety of attacks. Common issues include hostile attacks, unauthorized access, and data breaches. Large amounts of sensitive data are processed and stored in IoT cloud systems, which exacerbates these security problems. Technologies based on artificial intelligence provide creative ways to safeguard user data. Because AI-based predictive maintenance stops equipment problems before they happen, it lowers downtime and increases overall system reliability. Additionally, artificial intelligence (AI) makes it possible for sophisticated threat detection and response systems to identify anomalous patterns that point to weaknesses in system security and act swiftly to eliminate threats, guaranteeing ongoing operations. The convergence of cloud computing, machine learning, and the IoT offers unparalleled opportunities for data processing and real-time analysis. However, this integration also brings up significant privacy and security issues that must be addressed to reach its full potential. Federated Learning (FL), an important AI-based privacy-preserving technology, enables distributed training of machine learning models on local devices. Consequently, it ensures that the gadget will keep the raw data. This approach significantly secures private information and lowers the risk of data breaches by allowing devices to cooperate to improve a machine learning model. This research focuses on privacy preservation in cloud-IoT environments using AI-driven FL as they relate to AI-based resilience and privacy preservation in the IoT cloud context. In this chapter, we propose using Federated Learning (FL) to minimize the limitations of current AI techniques in IoT environments, which helps to enhance data privacy and security. FL protects user privacy and ensures robust model correctness by enabling decentralized machine learning model training without exchanging raw data. This approach facilitates safe and efficient data processing across various IoT applications. This helps to strengthen the scalability and efficiency of FL-based security solutions which has proved to be essential. Integrating multiple privacy protection technologies within the FL framework provides stronger protection against emerging threats, making FL pivotal for high performance and strict privacy in IoT.