# Federated Learning for Cybersecurity: A Privacy-Preserving Approach
## 3 Proposed Methodology
private dataset, using gradient clipping to limit the update size and Fisher-based parameter pruning to reduce dimensionality. These techniques aim to limit potential leakage from gradient inference and minimize communication overhead. After completing local training, clients encrypt model updates using a Diffie-Hellman key exchange mechanism and send the encrypted parameters to the aggregation server. Blockchain logging ensures that all updates are auditable and tamper-proof. The server performs secure multiparty aggregation to combine updates without reconstructing private data, followed by the injection of calibrated differential privacy noise to protect individual contributions before updating the global model. The system supports secure decentralized model training while implementing privacy-preserving mechanisms at both the client and server sides. Initially, a central server initializes a global model and distributes it to participating clients. Each client conducts local training on its private data without transmitting raw samples. To enhance security, local models employ gradient clipping and selective parameter sharing based on the Fisher information matrix [5], effectively reducing potential information leakage and communication overhead. During the local update phase, clients encrypt their model parameters using a secure Diffie-Hellman key exchange protocol [10], preventing interception attacks. This encrypted communication layer is supported by a blockchain infrastructure [11] to provide auditable, tamper-proof recording of model updates, increasing trust among participants. Upon receiving updates, the central server performs a robust aggregation using secure multi-party computation techniques [7], ensuring that no single party can reconstruct sensitive data from model parameters. Additionally, controlled differential privacy noise is injected into the aggregated model to further preserve client confidentiality [9]. To adapt to heterogeneous and dynamic IoT environments, the framework includes personalized FL mechanisms [8], enabling the system to fine-tune models according to client-specific data patterns while maintaining overall network security and stability. Furthermore, smart load balancer mechanisms are incorporated to manage communication efficiency and client sampling strategies [34]. The privacy-preserving structure also integrates secure edge-fog-cloud architecture models [1], utilizing techniques like encrypted federated analytics [23] and decentralized IoT gateway security [35]. As shown in Figure 2, an additional privacy control layer is used to protect client identities and model updates. To strengthen the cybersecurity defenses, techniques such as fine-grained access control for federated IoT data sharing [27], blockchain interoperability frameworks, and privacy-preserving vehicular positioning are implemented. Robust threat intelligence sharing over federated grids and post-quantum cryptographic methods like Dilithium  
Figure 2: Privacy and communication control layers in the proposed FL systems. are also embedded within the architecture to future-proof the system against emerging threats [36; 37]. Compared to traditional centralized machine learning solutions, the proposed methodology ensures high scalability, improved privacy guarantees, low communication costs, and resilience against adversarial participants. Its modular and adaptive design makes it practical for deployment in real-world settings like smart cities, autonomous vehicular networks, healthcare IoT environments, and cloud-edge infrastructures.