Element 2: TAFA Core: Trust‑Aware Federated Aggregation Engine

Benefit: 9/10  Effort: 8/10

depends on #1: AOI‑GBE Core: Generative Bayesian Ensemble for Robust Policy Inference

Recommendation - What To Do

Implement the MDRE, ADPL, ZKP, BLTL, QRAC, FGCLM, and ZSTTM modules, integrate them into the aggregation server, and validate the pipeline with a 10‑client heterogeneous testbed. Produce immutable audit logs, verify DP compliance, and demonstrate resilience to poisoning and Byzantine attacks.

Specific Benefits

Effort Profile

Dependencies & Prerequisites

• AOI‑GBE policy inference outputs and LLM‑based adversarial curriculum for realistic client data
• Shared data pipeline and secure aggregation primitives from Phase 1
• Access to a permissioned blockchain testnet (e.g., Hyperledger Besu) for ledger deployment
• Pre‑existing DP noise budget calibration tool

Step-by-Step Plan

1. Define reputation feature set (gradient norms, loss variance, content similarity, cryptographic attestations) and Bayesian update rule.
2. Implement MDRE module: compute per‑client reputation scores, apply soft‑exclusion weighting.
3. Build ADPL: scale DP noise inversely with reputation, generate zero‑knowledge proof of compliance for each update.
4. Deploy BLTL smart contract: record reputation, update hashes, and ZKP commitments; enable off‑chain roll‑ups for scalability.
5. Implement QRAC: compute Grover‑style amplitude amplification weights on client updates, validate on a classical simulator.
6. Build FGCLM: generate local graph embeddings, compute contrastive loss vectors, and expose weighted similarity scores.
7. Integrate ZSTTM: aggregate client policies using Bayesian trust metrics and expose a trust‑aware policy weighting interface.
8. Wire all modules into the aggregation server, exposing REST/GRPC endpoints for client update submission.
9. Spin up 10 heterogeneous client nodes (UAV, IoT, edge) and run a 2‑week test campaign with simulated poisoning and Byzantine attacks.
10. Verify DP compliance by checking ZKP proofs against on‑chain records; audit logs must be retrievable and tamper‑evident.
11. Measure model convergence, accuracy drop, and attack impact; iterate on reputation thresholds and DP budgets.
12. Package the pipeline into a Docker image, set up CI/CD, and deploy to the pilot environment.

Success Criteria

• Aggregated model accuracy within 5% of a centralized baseline after 20 training rounds.
• DP noise scale verified by ZKP proofs for ≥90% of client updates.
• Reputation scores updated correctly and reflected in aggregation weights for all clients.
• Audit logs are immutable, retrievable from the blockchain, and pass a third‑party audit within 48 hours.
• No critical security incidents (poisoning, Byzantine, privacy leakage) during the 2‑week test campaign.

Downstream Leverage

What This Enables

• Full TAFA pipeline ready for pilot deployment in Chapter 4 (RACE) and Chapter 11 (Retrieval‑Augmented Generation).
• Regulatory compliance documentation (EU AI Act, ISO/IEC 42001) for the federated learning component.
• Baseline for quantum‑resilient weighting and graph‑contrastive learning modules to be tuned in later phases.

What Can Be Deferred Once This Is Done

Risks & Mitigations