MATL: 12-Month Execution Plan¶

From Research Prototype → Production Middleware

🎯 Goal¶

Transform MATL from academic concept into: 1. Published research (IEEE S&P / USENIX Security) 2. Open-source middleware (licensable SDK) 3. Reference deployment (1000+ node testnet)

Q4 2025 (Nov-Dec): Research Validation¶

Milestone 1: Finalize Experimental Pipeline¶

Goal: Reproducible results showing >35% BFT tolerance

Week 1-2: Clean Up 0TML Codebase¶

0TML/
├── experiments/
│   ├── mode0_peer_comparison.py
│   ├── mode1_pogq_oracle.py
│   ├── mode2_pogq_tee.py
│   └── mode3_vsv_prototype.py (research track)
├── attacks/
│   ├── sign_flip.py
│   ├── gaussian_noise.py
│   ├── model_poisoning.py
│   └── sleeper_agent.py
├── metrics/
│   ├── detection_rate.py
│   ├── false_positive_rate.py
│   └── convergence_analysis.py
├── datasets/
│   ├── mnist.py
│   ├── cifar10.py
│   └── synthetic.py
└── tests/
    ├── test_mode0.py
    ├── test_mode1.py
    └── test_integration.py

Deliverables: - [ ] 95%+ test coverage - [ ] Reproducible results (seed-controlled) - [ ] Docker container for experiments - [ ] README with one-command setup

Week 3-4: Run Comprehensive Attack Matrix¶

Success Criteria: - Mode 0: Works ≤33% Byzantine - Mode 1: Works ≤45% Byzantine - Mode 1 outperforms FedAvg, Krum, FLTrust by 10%+

Week 5-6: Performance Benchmarking¶

# Measure overhead vs. centralized FL
Metric                  Centralized   Mode 0   Mode 1   Mode 2
Round Time              1.0×          1.1×     1.5×     2.0×
Convergence Epochs      100           105      110      120
Detection Latency       N/A           <1s      <5s      <10s
Memory Overhead         0%            5%       15%      25%

Deliverables: - [ ] Performance report - [ ] Scalability analysis (10 → 100 → 1000 nodes) - [ ] Cost analysis per node

Week 7-8: Paper Draft v1.0¶

Title: "Byzantine-Robust Federated Learning Beyond 33%: A Decentralized Approach"

Sections: 1. Introduction (problem statement) 2. Related Work (FedAvg, Krum, FLTrust comparison) 3. MATL Architecture (3 modes) 4. Experimental Setup 5. Results (attack matrix + performance) 6. Discussion (limitations, future work) 7. Conclusion

Target: Submit to IEEE S&P or USENIX Security

Q1 2026 (Jan-Mar): Open Source Release¶

Milestone 2: MATL SDK v0.1¶

Goal: Developers can integrate MATL in 1 hour

Week 9-12: Core SDK Implementation¶

# matl/__init__.py
from .client import MATLClient
from .validator import MATLValidator
from .protocols import Mode0, Mode1, Mode2

# Example usage
client = MATLClient(mode=Mode1, oracle_endpoint="https://oracle.example.com")
client.submit_gradient(gradient, metadata)
validation_result = client.verify_gradient(gradient_hash)

Features: - [ ] Clean Python API - [ ] Async support (asyncio) - [ ] Pluggable backends (HTTP, gRPC, WebSocket) - [ ] Type hints + docstrings - [ ] Example notebooks

Week 13-14: Holochain Integration¶

// matl-holochain/zomes/matl/src/lib.rs

#[hdk_extern]
pub fn submit_gradient_mode1(
    gradient_hash: Hash,
    pogq_score: f64,
    oracle_signature: Signature
) -> ExternResult<ActionHash> {
    // Validate oracle signature
    let oracle_pubkey = get_oracle_pubkey()?;
    verify_signature(&gradient_hash, &oracle_signature, &oracle_pubkey)?;

    // Store gradient with PoGQ score
    let entry = GradientEntry {
        gradient_hash,
        pogq_score,
        timestamp: sys_time()?,
        submitter: agent_info()?.agent_pubkey,
    };

    create_entry(EntryTypes::Gradient(entry))
}

#[hdk_extern]
pub fn get_gradient_trust_score(gradient_hash: Hash) -> ExternResult<f64> {
    // Retrieve and aggregate trust scores from DHT
    let entry = get(gradient_hash, GetOptions::default())?;
    Ok(entry.pogq_score)
}

Integration Pattern:

FL Client (PyTorch/TF) 
    ↓ gradient
MATL SDK (Python)
    ↓ HTTP/gRPC
Holochain Node (Rust)
    ↓ DHT gossip
Network Validation

Week 15-16: Documentation & Examples¶

# MATL Documentation

## Quick Start
pip install matl

## Examples
- [MNIST with Mode 1](examples/mnist_mode1.py)
- [CIFAR-10 with Holochain](examples/cifar10_holochain.py)
- [Custom Attack Detection](examples/custom_attack.py)

## API Reference
- [Client API](docs/api/client.md)
- [Validator API](docs/api/validator.md)
- [Protocol Modes](docs/protocols.md)

## Architecture
- [System Design](docs/architecture.md)
- [Holochain Integration](docs/holochain.md)
- [Performance Tuning](docs/performance.md)

Deliverables: - [ ] Documentation website (MkDocs) - [ ] 5+ tutorial notebooks - [ ] Video walkthrough (10 min) - [ ] Blog post announcement

Q2 2026 (Apr-Jun): Whitepaper & Partnerships¶

Milestone 3: MATL Whitepaper v1.0¶

Goal: Technical foundation for licensing & partnerships

Week 17-20: Whitepaper Drafting¶

Title: "MATL: Adaptive Trust Middleware for Decentralized Machine Learning"

Sections: 1. Abstract (1 page) - Problem: FL needs >33% BFT + decentralized validation - Solution: MATL's 3-mode architecture - Results: 45% tolerance demonstrated

1. Introduction (3 pages)
2. FL threat model
3. Limitations of existing work

4. MATL's contributions

5. Architecture (8 pages)

6. Mode 0: Peer comparison
7. Mode 1: PoGQ oracle
8. Mode 2: PoGQ + TEE
9. Mode 3: VSV (research preview)

10. Holochain + libp2p hybrid

11. Composite Trust Scoring (5 pages)

12. PoGQ: Validation accuracy
13. TCDM: Temporal/community diversity
14. Entropy: Behavioral randomness

15. Formula: Score = (PoGQ × 0.4) + (TCDM × 0.3) + (Entropy × 0.3)

16. RB-BFT Integration (4 pages)

17. Reputation-weighted voting
18. Mathematical proof of 45% tolerance

19. Cartel detection algorithms

20. Implementation (5 pages)

21. Python SDK
22. Holochain zomes
23. Performance benchmarks

24. Deployment patterns

25. Security Analysis (6 pages)

26. Attack scenarios
27. Detection rates
28. Failure modes

29. Mitigation strategies

30. Use Cases (4 pages)

31. Healthcare (privacy-preserving diagnostics)
32. Finance (fraud detection)
33. Defense (edge intelligence)

34. Research (academic collaborations)

35. Economic Model (3 pages)

36. Licensing tiers (Research / Non-Profit / Commercial)
37. Revenue projections

38. Open-source vs. proprietary components

39. Roadmap (2 pages)

    • Phase 1: SDK + testnet (2026)
    • Phase 2: Production deployments (2027)
    • Phase 3: Mycelix integration (2028+)

40. Conclusion (1 page)

Total: ~40 pages

Week 21-22: Design Assets¶

• System architecture diagrams (Figma)
• Trust flow visualizations
• Performance charts
• Comparison tables (MATL vs. competitors)

Week 23-24: Community Launch¶

Targets: - Hacker News / Reddit (r/MachineLearning) - Academic Twitter - ArXiv preprint - GitHub trending

Metrics: - 1000+ GitHub stars (month 1) - 10+ companies testing SDK - 3+ academic citations - 5+ blog post mentions

Q3 2026 (Jul-Sep): Testnet Deployment¶

Milestone 4: 1000-Node Reference Network¶

Goal: Prove MATL scales in production

Week 25-28: Testnet Infrastructure¶

# k8s/matl-testnet.yaml
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: matl-node
spec:
  replicas: 1000
  template:
    spec:
      containers:
      - name: holochain
        image: matl/holochain-node:v1.0
        resources:
          requests:
            cpu: 500m
            memory: 1Gi
      - name: fl-client
        image: matl/fl-client:v1.0
        env:
        - name: MODE
          value: "mode1"
        - name: ORACLE_ENDPOINT
          value: "https://oracle.matl-testnet.org"

Deployment: - 1000 nodes (AWS / GCP / Azure mix) - Geographic distribution (5 continents) - Heterogeneous hardware (CPU / GPU mix)

Week 29-32: Training Campaigns¶

Metrics Dashboard:

Real-time monitoring:
- Active nodes: 987 / 1000
- Current epoch: 45 / 100
- Detected Byzantine nodes: 23 (23%)
- Network accuracy: 94.3%
- Average round time: 12.4s

Week 33-36: Testnet Report¶

Deliverables: - [ ] Technical report (20 pages) - [ ] Performance data (CSV exports) - [ ] Lessons learned - [ ] Production readiness assessment

Q4 2026 (Oct-Dec): Commercialization¶

Milestone 5: First Licensing Deals¶

Goal: $100K ARR from MATL licensing

Week 37-40: Licensing Framework¶

# MATL Licensing Tiers

## Research License (Free)
- Academic use only
- Public datasets
- Publications require citation
- Community support

## Non-Profit License ($25K/year)
- NGOs, foundations, research institutions
- Private datasets allowed
- Email support
- No commercial deployment

## Commercial License ($100K+/year)
- Unlimited production use
- Custom SLAs
- Priority support
- Optional on-premise deployment
- Optional custom features

## Enterprise License (Custom)
- Dedicated engineering support
- Custom integrations
- Shared IP agreements
- Co-marketing opportunities

Week 41-44: Sales Pipeline¶

Target Industries: 1. Healthcare (Epic, Cerner, Philips) 2. Finance (JPMorgan, Goldman, Stripe) 3. Defense (Palantir, Booz Allen, MITRE) 4. Big Tech (Google FL, Meta, Microsoft)

Pitch Deck (15 slides): 1. Problem: FL is broken at scale 2. Market size: $2B+ by 2027 3. Solution: MATL middleware 4. Demo: Live testnet 5. Technology: Holochain + libp2p 6. Team & advisors 7. Traction: GitHub stars, testnet nodes, papers 8. Competition: FedAvg, Krum, FLTrust (comparison) 9. Business model: Licensing tiers 10. Use cases: Healthcare, finance, defense 11. Roadmap: SDK → Testnet → Production 12. Integration: 1 hour to deploy 13. Security: >45% BFT proven 14. Pricing: $100K/year enterprise 15. Ask: $500K seed round or first customer deal

Week 45-48: First Customer Onboarding¶

Ideal First Customer Profile: - Has existing FL deployment (pain point) - Willing to pilot new tech (innovation budget) - Can provide reference testimonial - $100K+ annual budget

Onboarding Plan: - Week 1: Architecture review - Week 2: SDK integration - Week 3-4: Pilot deployment (10-100 nodes) - Week 5-8: Production rollout - Week 9+: Ongoing support

Success Metrics by Quarter¶

Risk Mitigation¶

Risk 1: Paper Rejection¶

Probability: 30-40% (competitive venues)

Mitigation: - Submit to 2-3 venues in parallel - Have fallback venues (workshops, arxiv) - Use rejection feedback to improve

Fallback Plan: - Tech report + blog series - Focus on SDK adoption instead - Academic credibility via testnet

Risk 2: SDK Adoption Slow¶

Probability: 40-50% (new paradigm)

Mitigation: - Focus on documentation quality - Offer free consulting (first 10 users) - Create video tutorials - Build integrations (PyTorch, TensorFlow)

Fallback Plan: - Deploy own FL service (SaaS model) - Partner with existing FL platforms - Focus on one vertical (healthcare)

Risk 3: Testnet Performance Issues¶

Probability: 20-30% (infrastructure complexity)

Mitigation: - Start small (100 nodes) and scale gradually - Use cloud providers (AWS, GCP) - Monitor aggressively (Prometheus + Grafana) - Have expert devops support

Fallback Plan: - Reduce node count (500 nodes) - Use simulated network - Defer to Q1 2027

Risk 4: No Licensing Revenue¶

Probability: 60-70% (typical for new tech)

Mitigation: - Pursue grants (NSF, DARPA, EU Horizon) - Offer consulting services - Build reference customers (free pilots) - Create content (blog, videos, talks)

Fallback Plan: - Focus on open-source growth - Build community traction - Revenue deferred to 2027

Resource Requirements¶

Team (Minimum)¶

• 1 Research Engineer (FL expert)
• 1 Backend Engineer (Holochain/Rust)
• 1 DevOps Engineer (K8s, cloud)
• 1 Technical Writer (docs, blog)
• 1 Business Development (partnerships)

Budget: ~$500K/year (5 people × $100K)

Infrastructure¶

• Testnet: $10K/month (1000 nodes)
• Oracle services: $5K/month
• CI/CD: $2K/month
• Documentation hosting: $1K/month

Budget: ~$20K/month = $240K/year

Marketing¶

• Conference travel: $20K
• Content creation: $10K
• Sponsorships: $10K

Budget: ~$40K/year

Total Year 1: ~$780K

Funding Strategy¶

Phase 1: Bootstrap (Q4 2025)¶

• Self-funded or small grants ($50K)
• Focus on paper + MVP SDK

Phase 2: Seed Round (Q1-Q2 2026)¶

• Target: \(500K-\)1M
• Investors: Technical angels, research-focused VCs
• Use: Team + testnet + sales

Phase 3: Series A (Q4 2026 - Q1 2027)¶

• Target: \(3M-\)5M
• After: Published paper + testnet + first customers
• Use: Scale team + production deployments

Connection to Mycelix¶

Year 1 (2026): MATL as standalone middleware - Prove the tech works - Build credibility - Generate revenue

Year 2 (2027): Mycelix integration begins - MATL becomes Layer 6 - Add governance layers - Expand to full vision

Year 3+ (2028+): Full Mycelix Protocol - Constitutional governance - Collective intelligence - Civilization OS

Strategy: Use MATL success to fund and validate Mycelix vision.