Federated Learning in Cybersecurity: Privacy-Preserving Collaborative Models for Threat Intelligence Across Geopolitically Sensitive Organizational Boundaries

Abstract

As cyber threats become increasingly sophisticated and transnational, organizations face escalating challenges in sharing threat intelligence without compromising data confidentiality, national security interests, or regulatory compliance. Traditional centralized threat modeling systems require pooling sensitive telemetry and network data into unified repositories, raising concerns over surveillance, corporate espionage, and geopolitical tensions. These issues are particularly pronounced in scenarios involving cross-border organizations, government agencies, and multinational corporations operating under varying data sovereignty and privacy laws. Federated Learning (FL) offers a paradigm shift in collaborative cybersecurity by enabling multiple entities to train shared machine learning models without exchanging raw data. This decentralized approach facilitates privacy-preserving cooperation across geopolitically sensitive boundaries, allowing threat intelligence models to benefit from diverse datasets while preserving local control over confidential inputs. However, FL implementations in cybersecurity must address unique risks including adversarial participants, model poisoning, and inference attacks, particularly when participants operate under unequal trust assumptions. This paper explores the design and deployment of federated learning architectures for cybersecurity threat intelligence, focusing on secure model aggregation, differential privacy, and homomorphic encryption to prevent information leakage. We present use cases involving intrusion detection, malware classification, and anomaly detection across international financial institutions and governmental cybersecurity operations. Furthermore, we examine mechanisms for trust calibration, secure node authentication, and auditability to ensure integrity and transparency in federated collaborations. By bridging technical innovation with policy-aware safeguards, this study highlights the transformative potential of federated learning in global cyber defense enabling robust, real-time threat response while respecting jurisdictional boundaries and political sensitivities.