# Federated Learning for Cybersecurity: A Privacy-Preserving Approach
## 2 Related Work
### Main Contributions of This Work  
Previous studies have introduced FL frameworks, such as FedAvg [17], FedProx [18], and MOFL/MTFL [19], which have laid the groundwork for scalable collaborative learning. However, these approaches typically address privacy, efficiency, and security separately, resulting in gaps in resilience and adaptability in hostile environments. In this work, we propose a comprehensive, modular FL framework engineered to enhance cybersecurity in distributed, resource-constrained IoT environments. The framework combines a variety of privacy-preserving and performance-oriented techniques, including differential privacy, secure multi-party aggregation, gradient clipping, Fisher-based parameter pruning, and post-quantum encryption (Dilithium), into a unified, scalable system that can withstand adversarial interference. The components coexist and are orchestrated to ensure low-latency training, model convergence under non-IID data conditions, and robust protection of sensitive information at the data and model levels. Unlike conventional approaches, which tend to treat privacy, efficiency, and security as separate concerns, our proposed architecture takes a holistic design approach, embedding cryptographic protocols, model optimization heuristics, and auditability mechanisms directly into the operational fabric of the FL workflow. Blockchain-assisted logging mechanisms ensure traceability and tamper-proof recordkeeping among federated nodes. Meanwhile, adaptive edge-fog-cloud orchestration and personalized local updates significantly reduce communication overhead and model divergence. This layered integration enhances the system's ability to operate under dynamic threat landscapes and positions it as a forward-compatible solution capable of accommodating future enhancements, such as threat intelligence sharing and quantum-resilient cryptographic primitives. These contributions collectively define a comprehensive and scalable approach to privacy-preserving intrusion detection in federated IoT environments and provide the foundation for the methodology proposed in the next section. Table 1 provides a comparative overview of our proposed SecFL-IoT framework and well-established FL methods, such as FedAvg, FedProx, and MOFL/MTFL. The comparison centers on critical challenges in FL, including privacy leakage, communication overhead, and model poisoning. It explains the rationale behind the specific techniques used by each method and outlines the expected impact. The analysis shows how our framework improves security and privacy while enhancing scalability in distributed, heterogeneous environments. It is important to note that this comparison is conceptual and is based on the architectural designs and techniques described in the original works. The methods were not evaluated under identical experimental conditions; rather, they were analyzed based on their documented capabilities and design goals. As can be seen in Table 1, the proposed SecFL-IoT framework tackles key challenges in FL by combining well-established techniques from recent literature with customized mechanisms for IoT deployment. Differential privacy and secure aggregation mitigate privacy leakage by ensuring that raw data remains inaccessible to both the server and peers, as demonstrated in [1]. Communication overhead is reduced through parameter pruning and gradient clipping, which have been validated in edge AI contexts [2; 3]. Blockchain-based logging and client reputation scoring enforce accountability and robustness against model poisoning by using tamper-resistant ledgers and verifiable updates [4]. The inclusion of Fisher-based pruning supports model compression without compromising accuracy, which is essential for resource-constrained devices [5]. To handle data heterogeneity, personalized local updates and FedAvg variants facilitate generalization across non-IID datasets [6].