Federated Learning Approaches for Cross-Institutional Threat Detection Without Data Sharing Collaborative ML models for Financial Cybersecurity that Preserve Privacy
The sophistication of cyber threats targeting financial institutions continues to grow, necessitating the need for collaborative intelligence-sharing mechanisms that cross organizational boundaries but with a strict focus on protecting data confidentiality. Conventional centralized machine learning methods involve moving sensitive transactional and behavioral data, which creates unacceptable privacy risks, regulatory conflicts, and competitive vulnerabilities between financial entities. This paper introduces a federated learning framework leveraged in the cross-institutional cyber threat detection scenario, allowing several financial entities to collaboratively learn strong adversarial models while keeping their raw data within their local premises. Our proposed architecture utilizes differential privacy, secure multi-party computation, and homomorphic encryption guaranteeing cryptographic assurances upon model gradient aggregation. The local models are trained on the proprietary datasets of each participating institution, while only privacy-preserved (after applying differential privacy mechanisms) gradient updates will be sent to a secure aggregation server that synthesizes a globally optimized threat detection model. Specifically, the framework combines adversarial robustness strategies to reduce poisoning attacks based on federated training dynamics with Byzantine-fault-tolerant aggregation protocols that preserve model integrity in the presence of malicious participants. Based on our evaluation over simulated, multi-institutional financial environments, we show that the federated approach achieves both threat detection accuracy within 3.2% of centralized baselines while mitigating 100% risk of data exposure. This system has demonstrated the ability to generalize well across heterogeneous data distributions and has successfully detected zero-day fraud patterns, anomalous network intrusions, and insider threats. This work lays the foundation for a practical, scalable, and regulatory-compliant approach for the financial sector to leverage collaborative intelligence on cybersecurity without sacrificing institutional data sovereignty.