🏗️ Zero-TrustML Architecture¶

Technical architecture documentation for the Zero-TrustML (0TML) federated learning implementation.

🌟 Overview¶

Zero-TrustML is the production-grade implementation of Byzantine-resistant federated learning that achieves 45% Byzantine fault tolerance - exceeding the classical 33% limit.

📖 Architecture Documents¶

Core Architecture¶

• MATL Architecture - Mycelix Adaptive Trust Layer (primary architecture document)
• System Architecture - Complete system design
• FL Adapter - Federated learning adapter layer

Specialized Components¶

• Holochain Currency Exchange - Agent-centric value transfer
• ZK Proof of Concept - Zero-knowledge proof integration
• Meta Framework Vision - Future architecture vision

Trust & Security¶

• Beyond Algorithmic Trust - Trust model philosophy
• MATL Whitepaper - Academic foundation

API Documentation¶

• ZeroTrustML Credits API - API reference
• Credits Tutorial - Getting started guide

🎯 Key Architectural Innovations¶

1. Breaking the 33% Byzantine Barrier¶

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

Reputation-Weighted Validation

Byzantine_Power = Σ(malicious_reputation²)
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.

2. Composite Trust Scoring¶

Multiple validation mechanisms running in parallel:

• PoGQ (Proof of Gradient Quality): Statistical validation of model updates
• TCDM (Trust-Corrected Debiased Mean): Reputation-weighted aggregation
• Entropy Analysis: Detect coordinated attacks through information theory
• Cartel Detection: Graph-based clustering of suspicious nodes

3. Multi-Backend Architecture¶

Seamless hot-swapping between storage layers:

• PostgreSQL: Fast, centralized development
• Holochain DHT: Distributed, agent-centric production
• Ethereum: Immutable audit trail
• Cosmos: Cross-chain interoperability

4. Verifiable Computation¶

zk-STARK proofs ensure:

• Aggregation correctness
• Reputation updates are valid
• Byzantine detection is tamper-proof

📊 Performance Characteristics¶

🔗 Related Documentation¶

• 0TML Overview - Complete implementation guide
• Production Runbook - Operations guide
• System Architecture - Protocol-level design
• Whitepaper - Academic foundation

🚀 Getting Started¶

For implementation details and code examples, see:

• Installation: 0TML/docs/01-getting-started/INSTALLATION.md (in main repository)
• Developer Guide: 0TML/docs/03-developer-guide/README.md (in main repository)
• API Reference: 0TML/docs/04-api/README.md (in main repository)

See also: Complete Documentation Hub