Federated Learning Poisoning Prevention
Objective
Protect multi-party models from malicious clients.
Control / requirement
Gradient anomaly detection + robust aggregation.
Business impact
Failure to implement this control creates risk of global model poisoning via compromised federated learning clients, with estimated financial exposure of $2M-$10M for multi-organisation federated models.
Validation approach
Test ID: D1-CTL-04-VTS-001 Test Type: Hybrid (Automated + Manual Review) Test Design: Simulate malicious client submitting poisoned gradients in federated learning simulation environment Execution Steps: 1. Deploy GAISSF™ federated learning test harness 2. Configure with 10 client nodes, 1 malicious 3. Execute: pytest tests/d1_model_integrity/test_federated_poisoning.py -v # oda3-gaissf-vts 4. Review output for detection_rate and aggregation_audit Pass Criteria: malicious_gradient_detection_rate >= 95%; poisoned_gradients_excluded_from_aggregation = True Independent Verification: Auditor re-runs federated learning simulation with GAISSF™ test harness using auditor-controlled attack parameters.
Expected evidence
Not separately specified in the available source.
Mapping and source
Not separately specified in the available source.
Implementation guidance
Translate the requirement into system-specific procedures, responsible roles, technical configurations and review conditions. Retain evidence showing both design and operating performance. Where the source provides no separate implementation instruction, do not infer that a single technical mechanism is sufficient.
Assessment considerations
- Confirm scope and applicability.
- Inspect control design and responsible ownership.
- Test representative operation and adverse conditions where appropriate.
- Evaluate evidence provenance, completeness and contradictory evidence.
- Record limitations and notably absent outcomes.