# Federated Learning for Cybersecurity: A Privacy-Preserving Approach
## 2 Related Work  
The development of cyber safety threats and increasing demand for focused privacy solutions have significantly expanded research into FL applications. While traditional centralized machine learning methods remain strong, they show critical restrictions such as data leakage, narrow communication spots and exposure to individual points of failure. These disadvantages are particularly important in IoT ecosystems, where a huge number of connected devices work over sensitive data protection. The challenge of learning from non-IID data across heterogeneous clients has led to the development of multi-objective and multi-task FL strategies, with studies showing improved accuracy and fairness in real-world scenarios [6]. In parallel, efforts to integrate FL within distributed cloud computing infrastructures have leveraged SMPC, trusted execution environments, and differential privacy to provide robust and privacy-preserving analytics across nodes [7]. Personalization in FL has emerged as a key area, enabling the adaptation of models to individual clients while defending against advanced threats. Defense mechanisms based on gradient similarity and layered trust policies have shown improved robustness in countering stealthy backdoor attacks, without compromising collaborative learning [8]. In dynamic and containerized environments, AI-powered adaptive security meshes have been proposed as complementary to FL, improving threat detection and resilience [9]. Further developments in secure computation include efficient delegable order-revealing encryption schemes that support multi-user range queries over encrypted data--an essential capability in federated analytics frameworks [10]. To enhance trust and auditability, blockchain-integrated FL has also gained attention, enabling immutable logs and secure collaboration in decentralized learning systems [11]. Within smart cities, FL frameworks augmented with lightweight encryption and privacy-preserving mechanisms have been proposed to secure large-scale IoT infrastructures [12]. Expanding into vehicular networks, research has introduced certificateless signature schemes with batch verification for secure vehicle-to-vehicle communication, reducing computational overhead while ensuring privacy [17]. Risk modeling techniques, such as the Cyber Intelligent Risk Assessment (CIRA) [18] methodology, have combined machine learning with FL to estimate cyber risks in industrial IoT environments. Authentication mechanisms have also evolved through the application of FL, utilizing alternative biometric data such as energy consumption patterns for IoT device identification, thus reducing dependence on explicit user credentials [19]. Federated architecture has additionally been applied in secure ride-matching systems, enabling real-time privacy-preserving matching over road networks [20]. Homomorphic data encapsulation techniques for secure vehicular positioning have also been developed to maintain location privacy in smart transportation [21], while scalable cross-domain anonymous authentication mechanisms have supported robust FL deployment in IoT settings [22]. Other novel directions have included secure cross-modal search over encrypted datasets [23], Dilithium-based encryption integration for federated security [24], and privacy-preserving image retrieval systems tailored for FL applications [25]. Techniques for exposing IoT platforms securely behind Carrier-Grade NATs [26] and implementing fine-grained access control in cloud-assisted vehicular networks [27] have further reinforced FL's role in protecting distributed infrastructure. More recent advancements include client-sampled federated meta-learning strategies that personalize intrusion detection models across IoT devices [13], as well as hybrid transfer and self-supervised learning models aimed at improving network security in vehicular environments [28].