Matthew Vaishnav

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FAIR-WEIGHTS-H Implementation Status ​

Last Updated: May 22, 2026 Status: Experimental research implementation Validation: Requires empirical validation before clinical use

What Is Implemented ​

Core Weighting Engine (fair_weights_h.py) ​

✅ Implemented and tested

  • Hybrid scoring model combining quality, uniqueness, fairness, contribution, volume, and uncertainty
  • Stable softmax normalization with numerical stability guarantees
  • Weight caps and renormalization
  • Integrity gate for excluding institutions with data quality failures
  • Conservative mode that reduces diversity and fairness coefficients
  • Entropy and effective institution count diagnostics
  • Input validation for all signals

Test coverage: 8/8 tests passing (92% line coverage)

Synthetic Federation Utilities (synthetic_federation.py) ​

✅ Implemented and tested

  • Deterministic synthetic institution generators
  • Volume-based weighting baseline
  • Prestige-based weighting baseline (cancer center 2.0x, teaching 1.5x, community 1.0x, rural 0.8x)
  • Equal weighting baseline

Test coverage: 3/3 tests passing (88% line coverage)

Perturbation Scenarios (perturbations.py) ​

✅ Implemented and tested

  • Uncertainty spike simulation
  • Quality degradation simulation
  • Rare population enrichment simulation
  • Scanner shift simulation

Test coverage: 4/4 tests passing (100% line coverage)

Benchmark System (benchmark.py) ​

✅ Implemented and tested

  • Multi-strategy comparison framework
  • Before/after perturbation analysis
  • Weight delta, entropy delta, and effective N delta metrics

Test coverage: 1/1 tests passing (91% line coverage)

Experiment Runner (experiment_runner.py) ​

✅ Implemented and tested

  • Scenario execution across all weighting strategies
  • Result aggregation and reporting

Test coverage: 2/2 tests passing (100% line coverage)

Canonical Experiment Suite (experiment_suite.py) ​

✅ Implemented and tested

  • Pre-defined perturbation scenarios
  • Deterministic synthetic federation setup

Test coverage: 2/2 tests passing (100% line coverage)

Reporting (reporting.py, report_generator.py) ​

✅ Implemented and tested

  • Markdown table generation
  • Canonical experiment report generation
  • Interpretation guardrails

Test coverage: 2/2 tests passing (89-100% line coverage)

Weighted Aggregator (aggregator/weighted.py) ​

✅ Implemented and tested

  • FedAvg-style weighted aggregation
  • Integration with FAIR-WEIGHTS-H engine
  • Compatible with existing PathologyFL infrastructure

Test coverage: 3/3 tests passing (92% line coverage)

What Is NOT Implemented ​

Owen/Shapley Contribution Estimation ​

❌ Not implemented

The protocol specifies:

φᵢ = 𝔼[U(S∪{i}) - U(S)]

Current status: The contribution_score field in InstitutionWeightSignals accepts a scalar placeholder value. There is no actual Owen value computation, coalition sampling, or warm-started approximation.

Why: Owen/Shapley estimation requires:

  • A validation utility function U(S)
  • Coalition sampling infrastructure
  • Multi-membership group definitions
  • Significant computational cost

Workaround: Set contribution_score=0.0 (default) or use a proxy metric like gradient alignment (with appropriate caveats).

Difficulty-Adjusted Quality ​

❌ Not implemented

The protocol specifies:

logit(p_ij) = α + β'X_ij + b_i
A_i^adj = 𝔼[Pr(Y=1|X,i)]

Current status: The adjusted_quality field accepts a raw scalar. There is no difficulty adjustment model, case complexity features, or reference distribution calibration.

Why: Difficulty adjustment requires:

  • Standardized reference case set
  • Case complexity annotations (tumor type, stage, slide quality, etc.)
  • Hierarchical model fitting
  • Prospective validation

Workaround: Use raw accuracy on a balanced reference set, or manually adjust for known case mix differences.

Subgroup Constraints as Executable Optimizer ​

❌ Not implemented

The protocol specifies:

C_g(w) ≥ C_g^min  ∀g ∈ G_underserved
Perf_g(w) ≥ Perf_g^min  ∀g ∈ G_clinical

Current status: The weighting engine computes scores and applies caps, but does not solve a constrained optimization problem with subgroup performance or representation constraints.

Why: Subgroup constraints require:

  • Subgroup definitions and membership
  • Subgroup performance estimation
  • Constrained optimization solver (e.g., CVXPY)
  • Iterative validation

Workaround: Manually audit subgroup performance after weight computation and adjust caps or coefficients if needed.

Quarterly Algorithm and Versioning ​

❌ Not implemented

The protocol specifies a quarterly update cycle with:

  • Privacy-preserving aggregate signal collection
  • Anomaly detection
  • Versioned weights, coefficients, inputs, and audit results

Current status: The engine computes weights on demand from provided signals. There is no scheduling, versioning, or audit trail infrastructure.

Why: Production deployment infrastructure is out of scope for the initial research implementation.

Workaround: Manually version weight configurations and results in experiment tracking systems (e.g., MLflow, Weights & Biases).

Anomaly Monitoring and Fallback Modes ​

⚠️ Partially implemented

The protocol specifies continuous monitoring for:

  • Scanner drift
  • Stain distribution shift
  • Gradient drift
  • Validation collapse
  • Update variance anomalies

Current status: The conservative_mode flag reduces diversity and fairness coefficients, but there is no automated anomaly detection or mode switching.

Why: Anomaly detection requires:

  • Baseline distributions
  • Drift detection algorithms
  • Alert thresholds
  • Governance policies

Workaround: Manually enable conservative_mode=True when uncertainty is high or anomalies are suspected.

How to Run Tests ​

Run all FAIR-WEIGHTS-H tests: ​

bash
pytest tests/federated/test_fair_weights_h.py \
       tests/federated/test_weighted_aggregator.py \
       tests/federated/test_synthetic_federation.py \
       tests/federated/test_weighting_perturbations.py \
       tests/federated/test_weighting_benchmark.py \
       tests/federated/test_experiment_runner.py \
       tests/federated/test_experiment_suite.py \
       tests/federated/test_weighting_reporting.py \
       tests/federated/test_report_generator.py -v

Expected result: 27/27 tests passing

Run with coverage: ​

bash
pytest tests/federated/test_fair_weights_h.py --cov=src/features/federated/pathology_fl/weighting --cov-report=html

How to Generate Synthetic Report ​

python
from src.features.federated.pathology_fl.weighting.report_generator import (
    generate_canonical_experiment_report
)

report_md = generate_canonical_experiment_report()
print(report_md)

Or from command line:

bash
python -c "from src.features.federated.pathology_fl.weighting.report_generator import generate_canonical_experiment_report; print(generate_canonical_experiment_report())" > docs/FAIR_WEIGHTS_H_SYNTHETIC_REPORT.md

How to Use in Federated Training ​

python
from src.features.federated.pathology_fl.weighting.fair_weights_h import (
    FairWeightsHEngine,
    FairWeightsHConfig,
    InstitutionWeightSignals,
)
from src.features.federated.pathology_fl.aggregator.weighted import WeightedAggregator

# 1. Collect signals from institutions
signals = [
    InstitutionWeightSignals(
        institution_id="hospital_a",
        adjusted_quality=0.85,
        process_quality=0.90,
        useful_uniqueness=0.3,
        fairness_score=0.7,
        uncertainty_penalty=0.1,
        contribution_score=0.0,  # Placeholder until Owen implemented
        volume_factor=1000.0,
        integrity_ok=True,
    ),
    # ... more institutions
]

# 2. Compute weights
config = FairWeightsHConfig(conservative_mode=False)
engine = FairWeightsHEngine(config)
result = engine.compute(signals)

print(f"Weights: {result.weights}")
print(f"Entropy: {result.normalized_entropy:.3f}")
print(f"Effective N: {result.effective_institution_count:.1f}")

# 3. Use in aggregation
aggregator = WeightedAggregator(institution_weights=result.weights)
global_update = aggregator.aggregate(client_updates)

Known Limitations ​

Implementation Limitations ​

  1. No Owen/Shapley contribution estimation - uses placeholder scalar
  2. No difficulty-adjusted quality - uses raw quality scores
  3. No subgroup constraints - caps only, no constrained optimization
  4. No automated anomaly detection - manual conservative mode only
  5. No versioning or audit trail - manual tracking required

Validation Limitations ​

  1. Synthetic data only - no real multi-institutional validation
  2. No model training - weights computed but not used in actual FL
  3. No clinical outcomes - no patient-level or diagnostic metrics
  4. No fairness validation - subgroup performance not empirically measured
  5. No robustness testing - no gaming, missingness, or adversarial scenarios

Conceptual Limitations ​

  1. Validation set bias - utility function can encode institutional bias
  2. Gaming vulnerability - uniqueness can be gamed by case selection
  3. Incomplete observability - case complexity may be incompletely observed
  4. Policy tradeoffs - math cannot resolve all fairness vs. accuracy tradeoffs

Next Steps ​

Short-term (1-2 months) ​

  1. ✅ Complete core engine and tests
  2. ✅ Generate synthetic perturbation report
  3. ⏳ Integrate with Camelyon17 federated experiments
  4. ⏳ Compare against equal, volume, and prestige baselines
  5. ⏳ Measure global AUC, calibration, and worst-group sensitivity

Medium-term (3-6 months) ​

  1. ⏳ Implement approximate Owen contribution estimation
  2. ⏳ Add difficulty-adjusted quality with reference case set
  3. ⏳ Implement subgroup constraints as CVXPY optimization
  4. ⏳ Add automated anomaly detection and mode switching
  5. ⏳ Validate on PANDA and TCGA datasets

Long-term (6-12 months) ​

  1. ⏳ Multi-institutional prospective validation
  2. ⏳ Regulatory review and claim alignment
  3. ⏳ Production deployment infrastructure
  4. ⏳ Quarterly update cycle and versioning
  5. ⏳ Clinical utility and fairness validation

References ​

  • Protocol: docs/FAIR_WEIGHTS_HYBRID_PROTOCOL.md
  • Synthetic report: docs/FAIR_WEIGHTS_H_SYNTHETIC_REPORT.md
  • Implementation: src/features/federated/pathology_fl/weighting/
  • Tests: tests/federated/test_*weighting*.py
  • Aggregator: src/features/federated/pathology_fl/aggregator/weighted.py

Research documentation. Not clinical validation or regulatory clearance.

Computational pathology, federated oncology learning, and mathematical validation infrastructure.