Post-Quantum Secure Federated DeFi for Inclusive Banking

Battle Count: 50

The paper is primarily focused on financial inclusion and lending decisions rather than quantitative trading. However, it has tangential relevance: (1) the post-quantum security concerns apply equally to trading infrastructure and DeFi protocols; (2) the federated encrypted computation paradigm could be adapted for inter-institutional trading signal aggregation without revealing proprietary strategies; (3) the risk classification framework (approval/rejection/conditional) shares conceptual similarities with trading signal generation; (4) the lattice-based FHE and blockchain auditability components are directly applicable to secure algorithmic trading environments. The paper does not address market microstructure, alpha generation, portfolio optimization, or trading strategy development.

The implementation requires integrating multiple complex systems: (1) Lattice-based FHE with both RLWE (CKKS/BFV) and TRLWE (TFHE) schemes at 128-bit security, requiring specialized libraries (e.g., OpenFHE, SEAL, TFHE-rs); (2) Dempster-Shafer Theory belief fusion performed entirely on encrypted data with homomorphic addition, subtraction, multiplication, and Newton-Raphson reciprocal approximation; (3) NASA-IBM Prithvi GFM integration for geospatial inference; (4) Hybrid on-chain/off-chain architecture with Polygon L2 blockchain, IPFS distributed storage, and smart contracts for key management and audit logging; (5) Multi-party key generation and collective public key aggregation across banks; (6) Federated batch selection with exploration-exploitation scoring; (7) Cloud FHE server infrastructure. Each component is individually complex, and their secure integration represents a significant engineering challenge. The paper does not provide implementation code or reference to specific FHE libraries used.

The paper provides detailed cryptographic parameters (Table II: RLWE/TRLWE ring dimensions, error distributions, modulus sizes), hardware specifications (Azure VM 16 vCPU, 32 GiB RAM), and infrastructure architecture (Table I). However, no source code, dataset, or GitHub repository is mentioned. The case study uses proprietary bank data from four regional banks in Virginia, and the NASA-IBM Prithvi GFM is externally hosted. Reproducing the full end-to-end pipeline would require access to the specific bank datasets, the Prithvi model, and the custom FHE implementation. The mathematical formulations are detailed but implementation-specific details (e.g., exact FHE library used, smart contract code) are not provided.