Secure Federated Learning for Defense

Train models across allied forces and distributed units without moving raw, sensitive intelligence data across borders. Our architecture ensures all operational data remains within its sovereign jurisdiction, fully compliant with national security mandates and the EU AI Act.

Reduce the time-to-deployment for coalition-wide AI capabilities from months to weeks. By enabling secure parameter exchange instead of data exchange, allied units can collaboratively improve threat detection and intelligence models without lengthy data-sharing agreements.

Build more accurate and generalizable models by learning from diverse, real-world operational data across multiple theaters and environments. Federated learning aggregates insights from edge devices and classified networks without exposing the underlying data sources.

Deploy and continuously update AI directly on ruggedized edge hardware in disconnected environments. Our federated learning systems enable drones, vehicles, and tactical units to learn from local sensor data and contribute to a global model, even with intermittent connectivity.

Eliminate the central data repository as a high-value cyber target. Our decentralized federated learning architecture ensures there is no single vault of aggregated intelligence data, dramatically reducing the attack surface and impact of a potential breach.

Maintain a cryptographically verifiable ledger of all model contributions and updates. Our system provides full audit trails for compliance, proving which units contributed to a model's intelligence without revealing their underlying sensitive data.

Train unified threat models across allied intelligence agencies without sharing raw classified data. Our federated architecture enables secure parameter exchange, allowing NATO and Five Eyes partners to maintain data sovereignty while improving collective predictive accuracy for shared adversaries.

Deploy federated learning directly on ruggedized edge hardware in disconnected, intermittent, and low-bandwidth (DIL) environments. Enable forward-deployed units to collaboratively improve computer vision for target recognition or NLP for document translation using only local operational data.

Federate learning across intelligence silos—SIGINT, GEOINT, HUMINT—to build models that reveal hidden patterns without creating a centralized data lake. Our systems correlate signals intelligence with geospatial imagery and human reports to predict adversary movements while compartmentalizing source data.

Harden federated learning systems against model poisoning, Byzantine attacks, and membership inference. We implement secure aggregation, differential privacy, and robust anomaly detection to ensure the integrity of the global model, even if individual edge nodes are compromised.

Maintain full data lineage and model provenance for intelligence oversight. Our platform provides immutable logs of all federated training rounds, participant contributions, and model updates, ensuring compliance with DCID 6/3, ICD 503, and other intelligence community directives.

Dynamically incorporate new intelligence from emerging theaters or novel threat actors into existing models without retraining from scratch. Our transfer federated learning techniques allow knowledge gained in one classified domain to be securely adapted for another, accelerating response to evolving threats.