Post-Quantum Secure Federated DeFi for Inclusive Banking

Researchers propose a post-quantum secure federated DeFi framework that combines lattice-based cryptography with homomorphic encryption to enable collaborative lending between banks while protecting against future quantum computing threats. The system uses encrypted data processing and geospatial AI models to assess creditworthiness of underserved borrowers, tested on agricultural lending in rural Virginia.

The paper addresses two converging challenges in financial infrastructure: the advancing timeline of quantum computing capabilities that threaten current cryptographic standards, and the persistent exclusion of underserved populations from traditional lending systems. By introducing lattice-based fully homomorphic encryption within a federated DeFi framework, the authors propose a mechanism where multiple institutions can collaboratively assess borrower risk without exposing sensitive financial or personal data to each other or a central authority.

This work emerges from growing recognition across both government and private sectors that post-quantum cryptography represents an existential upgrade requirement for systems managing critical financial assets. The National Institute of Standards and Technology has been standardizing quantum-resistant algorithms, and projects like NASA-IBM's Prithvi GFM demonstrate how AI models can generate verifiable, location-based evidence for credit decisions. The use of geospatial data for agricultural lending particularly addresses rural lending gaps where traditional credit histories are sparse.

For the DeFi ecosystem, this research suggests a bridge between decentralized finance and traditional banking infrastructure through privacy-preserving computation. Banks could participate in lending consortiums without legal friction or data exposure risks. For investors and developers, the framework indicates growing viability of cryptographic primitives designed for post-quantum resilience in production finance systems. The agricultural focus tests the model's practical utility in genuine underserved markets rather than theoretical applications.

Key developments to monitor include whether this framework achieves regulatory approval for inter-bank data sharing, whether lattice-based FHE reaches performance efficiency suitable for real-time lending decisions, and whether similar models expand to other underserved lending categories beyond agriculture.

• →Lattice-based fully homomorphic encryption enables banks to compute credit assessments on encrypted data without decryption, preserving privacy in federated lending systems.
• →Post-quantum cryptography migration from theoretical concern to practical DeFi implementation addresses quantum computing timeline acceleration.
• →Geospatial AI models provide verifiable evidence for credit decisions in regions with limited traditional financial histories.
• →Federated DeFi architecture uses blockchain for tamper-proof auditing while maintaining encrypted data exchanges between institutions.
• →Agricultural lending pilot demonstrates how privacy-preserving computation can expand financial inclusion in underserved rural markets.