# Federated Learning for Cybersecurity: A Privacy-Preserving Approach
## 3 Proposed Methodology  
To address the challenges of data privacy, communication overhead, and model robustness in distributed cybersecurity systems, we propose an FL framework enhanced with lightweight privacy-preserving techniques. The IoT environment is the primary focus \begin{table}
\begin{tabular}{c c c} \hline \hline
**Challenge** & **Technique(s) Applied** & **Justification and Impact** \\ \hline Privacy leakage & Differential Privacy, Secure Aggregation & Prevents data reconstruction by third parties; protects data during aggregation. \\ Communication overhead & Gradient Clipping, Parameter Pruning & Reduces transmission volume; enables faster convergence and lower bandwidth usage. \\ Model poisoning & Blockchain Logging, Client Reputation Score & Ensures auditability; tracks anomalies in contributions to deter malicious updates. \\ Gradient explosion/instability & Gradient Clipping & Ensures stable convergence by bounding extreme gradient values. \\ Complexity for IoT deployment & Fisher-Based Parameter Pruning & Shrinks model size while preserving accuracy; ideal for resource-limited edge devices. \\ Non-IID data handling & Personalized Local Updates, FedAvg Variant & Enables better generalization across heterogeneous device data. \\ Accountability and trust deficit & Blockchain Audit Trail & Provides transparent and immutable logs of client actions and updates. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mapping FL challenges to applied techniques and their impact. of our approach for which intrusion detection and malware classification are performed. Here, due to the overwhelming data sensitivity and network heterogeneity, there are significant challenges that need to be exercised. Several FL architectures have been proposed in recent literature to address similar issues. These include FedAvg [17], FedProx [18], and MOFL/MTFL [19]. However, these frameworks typically use general-purpose aggregation schemes that offer limited privacy enhancements and minimal support for auditability or communication optimization. For example, FedAvg emphasizes simplicity and scalability; FedProx introduces regularization for non-IID data; and MOFL/MTFL integrates task-specific personalization. In contrast, our framework integrates a broader set of mechanisms, including blockchain-assisted audit logging and post-quantum encryption, making it more suitable for adversarial, privacy-sensitive IoT environments. To ensure robust aggregation in adversarial settings, the framework uses a combination of secure multi-party computation (SMPC) and gradient anomaly detection based on cosine similarity and update norm thresholds. Before accepting model updates from clients, the server evaluates each client's contribution to detect potential poisoning or deviation from the expected learning trajectory. Aggregation is then performed only on trusted updates, which are weighted by client reputation scores and consistency metrics. This strategy significantly mitigates the risk of model corruption while preserving convergence stability in non-IID scenarios [20; 21]. In FL environments, ensuring privacy requires architectural strategies that prevent the exposure of raw data and maintain trust among distributed clients. The proposed system meets these requirements by integrating cryptographic communication, selective model update mechanisms, and differential privacy within a secure aggregation framework. Each client independently trains a local model using its private dataset. Then, all updates are processed and combined in a secure and verifiable manner by a central server. Figure 1 shows the system's overall architecture and briefly describes the major components and their process of interaction with clients and the central aggregation server. During each training round, the central server initializes the global model and distributes it to a selected set of participating clients. Each client performs local training on its  
Figure 1: Privacy-preserving FL framework for intrusion detection and malware classification.