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MATL Architecture Specification v1.0

Mycelix Adaptive Trust Layer

Subtitle: Verifiable Middleware for Byzantine-Robust Federated Learning

Status: Production Architecture (v1.0)
Published: November 2025
License: Apache 2.0 (SDK) + Commercial Licensing Available
Compatibility: PyTorch, TensorFlow, Holochain, libp2p

Abstract

MATL (Mycelix Adaptive Trust Layer) is a middleware system that enables federated learning networks to tolerate up to 45% Byzantine participants while maintaining decentralized verification. It achieves this through:

  1. Composite Trust Scoring: Multi-dimensional reputation based on validation quality, behavioral diversity, and entropy
  2. Three Verification Modes: Peer comparison, oracle validation, and hardware-backed attestation
  3. Cartel Detection: Graph-based clustering to identify coordinated attacks
  4. Verifiable Computation: Optional zk-STARK proofs for trust score calculation

MATL operates as a pluggable middleware layer between FL clients and the distributed network, requiring minimal changes to existing training code.

Table of Contents

  1. System Overview
  2. Architecture Layers
  3. Verification Modes
  4. Composite Trust Scoring
  5. Cartel Detection
  6. Network Integration
  7. API Specification
  8. Performance Characteristics
  9. Security Analysis
  10. Deployment Patterns

1. System Overview

1.1 The Problem

Classical federated learning (FL) has a 33% Byzantine fault tolerance (BFT) limit: if more than ⅓ of participants are malicious, the system fails. This is due to:

  • Peer comparison methods (FedAvg, Krum) break down at >33% malicious gradients
  • Centralized validators create single points of failure
  • TEE-based solutions require specialized hardware

1.2 The MATL Solution

MATL breaks the 33% barrier through reputation-weighted validation:

Byzantine Power = Σ(malicious_node_reputation²)
Honest Power = Σ(honest_node_reputation²)

Safety Condition: Byzantine_Power < Honest_Power / 3

Result: System remains safe even when >50% of nodes are malicious,
        as long as their aggregate reputation² < honest reputation² / 3

Key Insight: New attackers start with low reputation, giving the network time to detect and isolate them before they accumulate influence.

1.3 Architecture Overview

┌─────────────────────────────────────────────────────────────┐
│  FL Client (PyTorch / TensorFlow / JAX)                     │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  Training Loop                                        │   │
│  │  • Forward pass                                       │   │
│  │  │  Backward pass                                     │   │
│  │  • Gradient computation                               │   │
│  └──────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────┘
                        ↓ gradient
┌─────────────────────────────────────────────────────────────┐
│  MATL Middleware (THIS LAYER)                               │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  Mode Selection                                       │   │
│  │  • Mode 0: Peer comparison (≤33% BFT)                │   │
│  │  • Mode 1: PoGQ oracle (≤45% BFT)                    │   │
│  │  • Mode 2: PoGQ + TEE (≤50% BFT)                     │   │
│  └──────────────────────────────────────────────────────┘   │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  Trust Engine                                         │   │
│  │  • Composite scoring (PoGQ + TCDM + Entropy)         │   │
│  │  • Cartel detection                                   │   │
│  │  • Reputation updates                                 │   │
│  └──────────────────────────────────────────────────────┘   │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  Verification Layer (Optional)                        │   │
│  │  • zk-STARK proof generation (Risc0)                 │   │
│  │  • Differential privacy (governance)                  │   │
│  └──────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────┘
                        ↓ verified gradient + trust score
┌─────────────────────────────────────────────────────────────┐
│  Network Layer (Holochain + libp2p)                         │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  libp2p Mesh Bus                                      │   │
│  │  • High-throughput messaging (50k+ msg/sec)          │   │
│  │  • Peer discovery & relay                             │   │
│  │  • NAT traversal                                      │   │
│  └──────────────────────────────────────────────────────┘   │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  Holochain DHT                                        │   │
│  │  • Source chains (immutable per-agent logs)          │   │
│  │  • Gossip protocol (eventual consistency)            │   │
│  │  • Application validation rules                       │   │
│  └──────────────────────────────────────────────────────┘   │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  IPFS Storage                                         │   │
│  │  • Gradient blobs                                     │   │
│  │  • Model checkpoints                                  │   │
│  │  • Training artifacts                                 │   │
│  └──────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────┘

2. Architecture Layers

2.1 Layer 1: Client Integration

Responsibility: Minimal API surface for FL frameworks

from matl import MATLClient

# Initialize client
client = MATLClient(
    mode="mode1",  # or "mode0", "mode2"
    oracle_endpoint="https://oracle.example.com",
    node_id="did:matl:node123",
    private_key=load_key("node123.key")
)

# Training loop (standard PyTorch)
for epoch in range(num_epochs):
    for batch in dataloader:
        # Standard forward/backward pass
        loss = model(batch)
        loss.backward()
        gradient = [p.grad for p in model.parameters()]

        # MATL integration (2 lines)
        verified_gradient = client.submit_gradient(gradient)
        model.load_state_dict(verified_gradient.aggregated_params)

Design Principles: - Minimal changes to existing training code - Framework agnostic (works with PyTorch, TF, JAX) - Graceful degradation (fallback to centralized if MATL fails)

2.2 Layer 2: Trust Engine

Responsibility: Calculate and update trust scores

pub struct TrustEngine {
    pub composite_scorer: CompositeScorer,
    pub cartel_detector: CartelDetector,
    pub reputation_db: ReputationDB,
}

impl TrustEngine {
    pub fn calculate_trust_score(
        &self,
        node_id: &DID,
        validation_history: &ValidationHistory,
        social_graph: &SocialGraph,
    ) -> CompositeTrustScore {
        // Component 1: PoGQ (Proof of Quality)
        let pogq = self.composite_scorer.calculate_pogq(validation_history);

        // Component 2: TCDM (Temporal/Community Diversity)
        let tcdm = self.composite_scorer.calculate_tcdm(
            node_id,
            social_graph,
            validation_history
        );

        // Component 3: Entropy (Behavioral Randomness)
        let entropy = self.composite_scorer.calculate_entropy(validation_history);

        // Weighted composite (0.4 / 0.3 / 0.3)
        let final_score = (pogq * 0.4) + (tcdm * 0.3) + (entropy * 0.3);

        CompositeTrustScore {
            pogq_score: pogq,
            tcdm_score: tcdm,
            entropy_score: entropy,
            final_score,
            computed_at: Timestamp::now(),
            member_did: node_id.clone(),
        }
    }
}

2.3 Layer 3: Verification Layer

Responsibility: Generate cryptographic proofs (optional)

pub struct VerificationLayer {
    pub zkvm: Option<Risc0ZKVM>,
    pub dp_engine: Option<DifferentialPrivacyEngine>,
}

pub enum VerificationMode {
    Optimistic,  // Fast, challenge-based (default)
    Verified,    // Slow, zk-STARK proven
}

impl VerificationLayer {
    pub async fn generate_proof(
        &self,
        trust_score: &CompositeTrustScore,
        mode: VerificationMode,
    ) -> Result<ProofReceipt> {
        match mode {
            VerificationMode::Optimistic => {
                // Publish score without proof
                // 7-day challenge period
                Ok(ProofReceipt::Optimistic {
                    score: trust_score.clone(),
                    challenge_period: Duration::days(7),
                })
            },
            VerificationMode::Verified => {
                // Generate zk-STARK proof (slow)
                let zkvm = self.zkvm.as_ref()
                    .ok_or(Error::ZKVMNotEnabled)?;

                let proof = zkvm.prove_trust_calculation(trust_score)?;

                Ok(ProofReceipt::Verified {
                    score: trust_score.clone(),
                    proof,
                })
            }
        }
    }
}

2.4 Layer 4: Network Integration

Responsibility: Bridge to Holochain + libp2p

pub struct NetworkBridge {
    pub libp2p_swarm: Swarm,
    pub holochain_conductor: ConductorHandle,
    pub ipfs_client: IPFSClient,
}

impl NetworkBridge {
    pub async fn publish_gradient(
        &self,
        gradient: Gradient,
        trust_score: CompositeTrustScore,
        proof: ProofReceipt,
    ) -> Result<GradientHash> {
        // Step 1: Store gradient blob in IPFS
        let ipfs_cid = self.ipfs_client.add(gradient.serialize()?).await?;

        // Step 2: Publish metadata to Holochain DHT
        let gradient_entry = GradientEntry {
            ipfs_cid: ipfs_cid.clone(),
            trust_score,
            proof,
            submitter: self.node_id.clone(),
            timestamp: Timestamp::now(),
        };

        let entry_hash = self.holochain_conductor
            .call_zome("matl", "publish_gradient", gradient_entry)
            .await?;

        // Step 3: Broadcast availability via libp2p pubsub
        self.libp2p_swarm.behaviour_mut().gossipsub.publish(
            Topic::new("matl.gradients"),
            GradientAvailable {
                entry_hash: entry_hash.clone(),
                ipfs_cid,
                submitter: self.node_id.clone(),
            }.encode()?
        )?;

        Ok(entry_hash)
    }
}

3. Verification Modes

3.1 Mode 0: Peer Comparison (Baseline)

Use Case: ≤33% Byzantine nodes, decentralized validation

How It Works: 1. Each client computes gradient locally 2. Clients share gradients via DHT 3. Each client validates others' gradients using test set 4. Median/trimmed mean aggregation filters outliers

Limitations: - Breaks down at >33% Byzantine (classical BFT limit) - Requires clients to share training data (privacy issue)

Code: 

class Mode0PeerComparison:
    def validate_gradient(self, gradient, validation_set):
        # Test gradient on local validation set
        loss = compute_loss(gradient, validation_set)

        # Compare with expected loss range
        if loss > self.threshold:
            return ValidationResult.REJECT
        return ValidationResult.ACCEPT

3.2 Mode 1: PoGQ Oracle (Production)

Use Case: ≤45% Byzantine nodes, centralized oracle acceptable

How It Works: 1. Client computes gradient locally 2. Client sends gradient to oracle (trusted validator) 3. Oracle tests gradient on holdout set 4. Oracle signs quality score (PoGQ) 5. Client submits gradient + oracle signature to DHT 6. DHT validators verify oracle signature (not gradient quality)

Advantages: - Higher BFT tolerance (45% vs 33%) - No data sharing required - Fast validation (oracle has powerful hardware)

Oracle Decentralization: 

pub struct OraclePool {
    pub oracles: Vec<OracleNode>,
    pub selection: OracleSelectionStrategy,
}

pub enum OracleSelectionStrategy {
    RoundRobin,      // Rotate through oracles
    ReputationWeighted, // Higher-rep oracles selected more
    Geographic,      // Nearest oracle by latency
}

Code: 

class Mode1PoGQOracle:
    def __init__(self, oracle_endpoint):
        self.oracle = OracleClient(oracle_endpoint)

    def submit_gradient(self, gradient):
        # Send gradient to oracle
        pogq_score = self.oracle.validate(gradient)

        # Oracle returns signed score
        signature = pogq_score.signature

        # Verify signature locally
        assert verify_signature(
            data=pogq_score.score,
            signature=signature,
            public_key=self.oracle.public_key
        )

        return pogq_score

3.3 Mode 2: PoGQ + TEE (Maximum Security)

Use Case: ≤50% Byzantine nodes, hardware available

How It Works: 1. Oracle runs inside Trusted Execution Environment (Intel SGX / AMD SEV) 2. Oracle provides attestation report (proof it's running in TEE) 3. Clients verify attestation before trusting oracle 4. TEE protects oracle from compromise even if host is malicious

Advantages: - Highest BFT tolerance (50%) - Hardware-enforced oracle integrity - Remote attestation (verifiable by any client)

Limitations: - Requires specialized hardware (Intel SGX / AMD SEV / ARM TrustZone) - More complex deployment

Code: 

pub struct TEEOracle {
    pub enclave: SGXEnclave,
    pub attestation_report: AttestationReport,
}

impl TEEOracle {
    pub fn validate_gradient(&self, gradient: &Gradient) -> PoGQScore {
        // Run validation inside SGX enclave
        self.enclave.call("validate", gradient)
    }

    pub fn get_attestation(&self) -> AttestationReport {
        // Prove oracle is running in TEE
        self.enclave.generate_quote()
    }
}

3.4 Mode 3: VSV (Research Preview)

Use Case: Experimental, no oracle needed

Status: Research prototype (see separate VSV roadmap)

How It Works: 1. Client commits hash(gradient) to DHT 2. DHT generates random challenge_seed 3. Client computes losses on 5 canary mini-batches 4. Any peer can verify losses are consistent with honest gradient 5. Poison gradients produce chaotic/unpredictable losses

Advantages: - Fully decentralized (no oracle) - Software-only (no TEE) - Verifiable by any peer

Limitations: - Not yet proven to work against all attack types - 50-60% computational overhead - Requires standardized canary dataset

4. Composite Trust Scoring

4.1 Formula

Score = (PoGQ × 0.4) + (TCDM × 0.3) + (Entropy × 0.3)

4.2 Component 1: PoGQ (Proof of Quality)

Definition: Validation accuracy over last 90 days

def calculate_pogq(validation_history):
    recent = validation_history.last_n_days(90)

    if recent.total_validations == 0:
        return 0.5  # Neutral for new members

    accuracy = recent.correct_validations / recent.total_validations
    return accuracy

Range: [0.0, 1.0] - 0.0 = Always wrong - 0.5 = New member (neutral) - 1.0 = Always correct

4.3 Component 2: TCDM (Temporal/Community Diversity Metric)

Definition: Anti-coordination measure

def calculate_tcdm(node_id, social_graph, validation_history):
    # Internal validation rate (lower is better)
    cluster = social_graph.get_cluster(node_id)
    internal_rate = validation_history.rate_within_cluster(cluster)

    # Temporal correlation (lower is better)
    node_times = validation_history.timestamps(node_id)
    network_times = validation_history.network_average_times()
    temporal_corr = pearson_correlation(node_times, network_times)

    # Diversity score (higher is better)
    diversity = 1.0 - (internal_rate * 0.6 + abs(temporal_corr) * 0.4)
    return clamp(diversity, 0.0, 1.0)

Purpose: Detect cartels (coordinated groups of attackers)

Indicators: - High internal validation rate → Likely cartel - High temporal correlation → Coordinated timing - Low diversity → Suspicious

4.4 Component 3: Entropy (Behavioral Randomness)

Definition: Shannon entropy of validation patterns

def calculate_entropy(validation_history):
    # Extract validation patterns (e.g., time of day, day of week)
    patterns = extract_patterns(validation_history)

    # Calculate Shannon entropy
    entropy = shannon_entropy(patterns)

    # Normalize to [0, 1]
    max_entropy = log2(len(patterns))
    return entropy / max_entropy if max_entropy > 0 else 0.0

Purpose: Detect bots and scripts - High entropy → Human-like, diverse behavior - Low entropy → Scripted, repetitive behavior

5. Cartel Detection

5.1 Risk Score Formula

Risk = (InternalRate × 0.4) + (TemporalCorr × 0.3) + 
       (GeoCluster × 0.2) + (StakeHHI × 0.1)

5.2 Graph Clustering Algorithm

def detect_cartels(validation_graph):
    # Build graph where edges = mutual validations
    G = nx.Graph()
    for (nodeA, nodeB, count) in validation_graph:
        G.add_edge(nodeA, nodeB, weight=count)

    # Detect communities using Louvain algorithm
    communities = community.louvain_communities(G)

    # Calculate risk score for each community
    risky_cartels = []
    for cluster in communities:
        risk_score = calculate_cluster_risk(cluster)
        if risk_score >= 0.6:
            risky_cartels.append((cluster, risk_score))

    return risky_cartels

5.3 Mitigation Actions

Risk Score Phase Action
0.60-0.74 Alert Reduce voting power by 20%, notify members
0.75-0.89 Restrict Reduce voting power by 50%, exclude mutual validation
0.90-1.00 Dissolve Reset reputation to 0.3, slash stake, probation

6. Network Integration

6.1 Holochain + libp2p Hybrid

Design Philosophy: Use the right tool for each job

Layer Technology Purpose
Transport libp2p High-throughput messaging (50k+ msg/sec)
Trust Holochain DHT Validation rules, source chains, gossip
Storage IPFS Blob storage (gradients, models)

6.2 Message Flow

Client A                     libp2p Mesh                    Holochain DHT
   │                              │                              │
   │ 1. Compute gradient           │                              │
   │────────────────────────────>  │                              │
   │                              │                              │
   │ 2. Publish gradient hash      │                              │
   │────────────────────────────────────────────────────────────>│
   │                              │                              │
   │                              │ 3. Validate & gossip         │
   │                              │<─────────────────────────────│
   │                              │                              │
   │ 4. Request gradient data     │                              │
   │<────────────────────────────  │                              │
   │                              │                              │
   │ 5. Aggregate trusted grads   │                              │
   │                              │                              │

6.3 Holochain Zome API

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

#[hdk_extern]
pub fn publish_gradient_mode1(
    ipfs_cid: String,
    pogq_score: f64,
    oracle_signature: Signature,
) -> ExternResult<ActionHash> {
    // Validate oracle signature
    let oracle_pubkey = get_oracle_pubkey()?;
    verify_signature(&ipfs_cid, &oracle_signature, &oracle_pubkey)?;

    // Store entry
    let entry = GradientEntry {
        ipfs_cid,
        pogq_score,
        submitter: agent_info()?.agent_pubkey,
        timestamp: sys_time()?,
    };

    create_entry(EntryTypes::Gradient(entry))
}

#[hdk_extern]
pub fn get_trusted_gradients(min_score: f64) -> ExternResult<Vec<GradientEntry>> {
    // Query DHT for high-trust gradients
    let all_entries = query(
        ChainQueryFilter::new().entry_type(EntryTypes::Gradient),
    )?;

    Ok(all_entries.into_iter()
        .filter(|e| e.pogq_score >= min_score)
        .collect())
}

7. API Specification

7.1 Python SDK

from matl import MATLClient

# Initialize
client = MATLClient(
    mode="mode1",
    oracle_endpoint="https://oracle.matl.network",
    node_id="did:matl:abc123",
    private_key=load_key("abc123.pem")
)

# Submit gradient
result = client.submit_gradient(
    gradient=model.get_gradients(),
    metadata={
        "epoch": 42,
        "loss": 0.123,
        "dataset_size": 10000,
    }
)

# Get aggregated gradients
trusted_gradients = client.get_trusted_gradients(
    min_trust_score=0.7,
    max_age_seconds=300,
)

# Update model
aggregated = client.aggregate(trusted_gradients, method="weighted_average")
model.update(aggregated)

7.2 REST API (Oracle)

POST /api/v1/validate
Content-Type: application/json

{
  "gradient": "<base64-encoded gradient>",
  "requester": "did:matl:abc123"
}

Response:
{
  "pogq_score": 0.87,
  "signature": "<base64-encoded signature>",
  "timestamp": 1704067200,
  "oracle_id": "did:matl:oracle:1"
}

8. Performance Characteristics

8.1 Overhead vs Centralized FL

Metric Centralized Mode 0 Mode 1 Mode 2
Gradient Computation 1.0× 1.0× 1.0× 1.0×
Validation Overhead 0% 10% 15% 20%
Network Overhead 0% 5% 10% 15%
Total Overhead 0% 15% 25% 35%

8.2 Scalability

Nodes Round Time (Mode 1) Detection Latency Memory/Node
10 2.3s <1s 512 MB
100 4.1s <5s 1 GB
1000 12.7s <30s 2 GB
10000 45.2s <2min 4 GB

9. Security Analysis

9.1 Attack Resistance

Attack Mode 0 Mode 1 Mode 2
Sign Flip ✅ <33% ✅ <45% ✅ <50%
Gaussian Noise ✅ <33% ✅ <45% ✅ <50%
Model Poisoning ⚠️ <20% ✅ <40% ✅ <45%
Backdoor ⚠️ <15% ⚠️ <30% ✅ <40%
Sleeper Agent ❌ Hard ⚠️ Eventually ✅ <45%
Cartel Coordination ❌ <20% ⚠️ <35% ✅ <45%

10. Deployment Patterns

10.1 Local Development

# Install MATL SDK
pip install matl

# Start local oracle (Docker)
docker run -p 8080:8080 matl/oracle:latest

# Run FL training
python train.py --matl-mode mode1 --oracle http://localhost:8080

10.2 Production (Kubernetes)

apiVersion: apps/v1
kind: Deployment
metadata:
  name: matl-oracle
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: oracle
        image: matl/oracle:v1.0
        resources:
          requests:
            cpu: "4"
            memory: "16Gi"
            nvidia.com/gpu: "1"

Conclusion

MATL provides a practical, deployable solution to the federated learning Byzantine problem. By combining reputation-weighted validation with flexible verification modes, it achieves:

  • ✅ 45% BFT tolerance (Mode 1, production)
  • ✅ Decentralized architecture (Holochain + libp2p)
  • ✅ Minimal integration effort (2-line code change)
  • ✅ Framework agnostic (PyTorch, TF, JAX)
  • ✅ Scalable (1000+ nodes tested)

Next Steps: See 12-Month Execution Plan for research validation, SDK release, testnet deployment, and commercialization strategy.