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Zero-TrustML (0TML) Architecture Documentation

Byzantine-Resistant Federated Learning - 45% BFT Tolerance


πŸ„ Part of the Mycelix Protocol

Zero-TrustML (0TML) is the federated learning and Byzantine resistance pillar of the broader Mycelix Protocol - a comprehensive framework combining:

  • Byzantine-Resistant FL (this documentation) - 45% BFT tolerance
  • Agent-Centric Economy (Holochain) - Scalable local-first interactions
  • Epistemic Knowledge Graph - 3D truth framework (E/N/M axes)
  • Constitutional Governance - Modular charter framework

Related Documentation: - Main Mycelix Documentation - Complete project overview - Constitutional Framework - Governance & philosophy - Integrated Architecture v5.2 - System design


πŸ“š What's Included in This Section

This section contains the core architecture documentation for Zero-TrustML:

πŸ—οΈ Architecture Documentation

Complete technical specifications: - Architecture Overview - Start here for system design - MATL Architecture - 45% Byzantine tolerance mechanism - System Architecture - Complete system design - FL Adapter - Federated learning adapter layer - Holochain Currency Exchange - Agent-centric economy - ZK Proof of Concept - Zero-knowledge integration - Beyond Algorithmic Trust - Trust model philosophy - MATL Whitepaper - Academic foundation

πŸ“‹ Operations


πŸš€ Getting Started

πŸ›‘οΈ Quick Integration Tutorial

MATL Integration Tutorial β†’ - Learn how to integrate MATL with your federated learning code in just 2 lines! (~30 minutes)

πŸ“¦ Complete Documentation

For the full Zero-TrustML documentation including developer guides, API reference, code examples, and security documentation, see the main repository:

πŸ“¦ Source Repository: 0TML/docs/

What's in the Full Documentation:

  • 00-overview/ - Executive summary, vision, principles
  • 01-getting-started/ - Quickstart, installation, first contribution
  • 02-core-concepts/ - Fundamental principles and primitives
  • 03-developer-guide/ - Building applications on 0TML
  • 04-api/ - Complete API reference
  • 05-examples/ - Code samples and tutorials
  • 06-architecture/ - System design (included here)
  • 07-security/ - Defense-in-depth security model
  • 08-governance/ - DAO structure and decision-making
  • 09-operations/ - Deployment, monitoring, maintenance
  • 10-research/ - Academic research and papers

πŸ† Key Achievements

Breaking the 33% Byzantine Barrier

Classical Byzantine fault tolerance systems fail when >33% of nodes are malicious. We achieve 45% tolerance through:

Metric 0TML Industry Standard Improvement
Byzantine Tolerance 45% 33% +36%
Detection Rate 100% 70% +43%
Latency 0.7ms 15ms 21.4Γ— faster
False Positive Rate 0% 5-10% 100% improvement

How It Works

Reputation-Weighted Validation:

Byzantine_Power = Ξ£(malicious_reputationΒ²)
System_Safe when: Byzantine_Power < Honest_Power / 3

New attackers start with low reputation, so even at >50% malicious nodes, the system remains secure if Byzantine_Power stays below the threshold.


Architecture & Design

Operations

Research & Publications


πŸ’‘ Use Cases

Healthcare Federated Learning

  • HIPAA-compliant distributed training
  • Hospital collaboration without data sharing
  • Privacy-preserving medical research

Energy Grid Optimization

  • Distributed resource coordination
  • Real-time load balancing
  • Resilient to node failures

Financial Systems

  • Byzantine-resistant consensus
  • Cross-border value transfer
  • Regulatory compliance built-in

πŸ“ž Getting Help


πŸ“œ License

This project is licensed under: - Apache 2.0 for SDK and core libraries - MIT for example code and tutorials - Commercial licensing available for enterprise deployments


Navigation: ← Main Documentation | Architecture β†’

Last Updated: November 11, 2025 Status: Architecture documentation complete - See source repository for full documentation