ZIP-7: BitDelta + DeltaSoup - Personalized and Community AI

Abstract

This proposal specifies Zoo's dual approach to AI model improvement: BitDelta for efficient per-user personalization and DeltaSoup for community-aggregated enhancements. BitDelta compresses user-specific fine-tune deltas to 1-bit signs plus per-tensor scales, achieving 10× memory savings while preserving safety properties. DeltaSoup aggregates signed deltas from many users via Byzantine-robust averaging to create reproducible Reference Adapters that benefit the entire network.

Motivation

Current AI systems face a fundamental tension:

Personalization requires privacy: Users want AI tailored to them without exposing personal data
Improvement requires sharing: Models get better by learning from many users
Efficiency requires compression: Serving millions of personalized models is prohibitively expensive
Safety requires verification: Changes must not degrade alignment or introduce backdoors

Zoo solves this quadrilemma through:

BitDelta: Private, compressed per-user adapters in secure enclaves
DeltaSoup: Opt-in community aggregation with privacy guarantees
GPU-CC: Hardware-enforced confidential computing
Robust Aggregation: Byzantine-fault-tolerant delta merging

Specification

BitDelta: Per-User Efficient Personalization

Mathematical Foundation

For user u with base model weights W_b and fine-tuned weights W_f:

Delta: δ_u = W_f - W_b
BitDelta: B_u = sign(δ_u), γ_u = scale(δ_u)
Reconstruction: W'_f ≈ W_b + γ_u * B_u

Compression Algorithm

def compress_to_bitdelta(base_weights, finetuned_weights):
    """
    Compress weight delta to 1-bit + scale representation
    Returns 10× compression with <10% performance degradation
    """
    delta = {}
    signs = {}
    scales = {}
    
    for layer_name, W_b in base_weights.items():
        W_f = finetuned_weights[layer_name]
        
        # Compute delta
        δ = W_f - W_b
        
        # Extract sign (1-bit)
        signs[layer_name] = torch.sign(δ).to(torch.int8)
        
        # Compute optimal scale via logit matching
        scales[layer_name] = compute_optimal_scale(
            base=W_b,
            delta=δ,
            calibration_set=user_data_sample
        )
    
    return BitDeltaAdapter(signs, scales)

def compute_optimal_scale(base, delta, calibration_set):
    """
    Find scale γ that minimizes KL divergence on calibration set
    """
    def loss(γ):
        W_approx = base + γ * torch.sign(delta)
        logits_orig = model_forward(base + delta, calibration_set)
        logits_approx = model_forward(W_approx, calibration_set)
        return kl_divergence(logits_orig, logits_approx)
    
    γ_optimal = optimize(loss, init=delta.abs().mean())
    return γ_optimal

Safety Preservation

Quantized deltas preserve alignment better than full fine-tuning:

def verify_safety_preservation(base_model, bitdelta):
    """
    Ensure BitDelta doesn't degrade safety properties
    """
    safety_benchmarks = [
        "truthfulness",    # TruthfulQA
        "harmlessness",    # HarmBench
        "helpfulness",     # MT-Bench
        "privacy",         # PrivacyBench
    ]
    
    base_scores = evaluate(base_model, safety_benchmarks)
    adapted_scores = evaluate(base_model + bitdelta, safety_benchmarks)
    
    # Require no more than 5% degradation
    for metric, base_score in base_scores.items():
        assert adapted_scores[metric] >= 0.95 * base_score
    
    return True

DeltaSoup: Community-Aggregated Intelligence

Robust Aggregation Protocol

def create_deltasoup(
    deltas: List[BitDeltaAdapter],
    attestations: List[TEEAttestation],
    privacy_budgets: List[PrivacyBudget]
) -> ReferenceAdapter:
    """
    Aggregate user deltas into community Reference Adapter
    using Byzantine-robust methods
    """
    
    # Step 1: Filter by attestation and privacy
    verified_deltas = []
    for delta, attest, privacy in zip(deltas, attestations, privacy_budgets):
        if verify_tee_attestation(attest) and privacy.epsilon <= MAX_EPSILON:
            verified_deltas.append(delta)
    
    # Step 2: Robust aggregation (coordinate-wise trimmed mean)
    def trimmed_mean(tensor_list, trim_ratio=0.1):
        stacked = torch.stack(tensor_list)
        n = len(tensor_list)
        k = int(n * trim_ratio)
        
        # Sort and trim outliers
        sorted_vals, _ = torch.sort(stacked, dim=0)
        trimmed = sorted_vals[k:n-k]
        
        return trimmed.mean(dim=0)
    
    # Aggregate each layer separately
    aggregated_signs = {}
    aggregated_scales = {}
    
    for layer_name in verified_deltas[0].signs.keys():
        layer_signs = [d.signs[layer_name] for d in verified_deltas]
        layer_scales = [d.scales[layer_name] for d in verified_deltas]
        
        aggregated_signs[layer_name] = trimmed_mean(layer_signs)
        aggregated_scales[layer_name] = trimmed_mean(layer_scales)
    
    # Step 3: Greedy soup mixing with safety gates
    reference = greedy_soup_mixing(
        base_model=get_base_model(),
        candidates=verified_deltas,
        eval_fn=benchmark_performance,
        safety_fn=verify_safety_preservation
    )
    
    # Step 4: Record contributors for royalties (using Lux ID)
    contributors = [d.user_lux_id for d in verified_deltas]  # did:lux:122:0x...
    contribution_weights = calculate_contribution_weights(verified_deltas)
    
    return ReferenceAdapter(
        signs=aggregated_signs,
        scales=aggregated_scales,
        contributors=contributors,
        weights=contribution_weights,
        version=compute_version_hash()
    )

Greedy Soup Mixing

def greedy_soup_mixing(base_model, candidates, eval_fn, safety_fn, step_size=0.25):
    """
    Iteratively mix deltas that improve performance without compromising safety
    Following Model Soups (Wortsman et al. 2022)
    """
    current_weights = base_model.state_dict()
    selected = []
    mixing_coefficients = []
    
    for candidate in candidates:
        # Try adding this delta
        test_weights = {}
        for k in current_weights:
            test_weights[k] = current_weights[k] + step_size * candidate.get_delta(k)
        
        # Evaluate performance and safety
        if safety_fn(test_weights) and eval_fn(test_weights) > eval_fn(current_weights):
            current_weights = test_weights
            selected.append(candidate)
            mixing_coefficients.append(step_size)
    
    return current_weights, selected, mixing_coefficients

Privacy-Preserving Training

Differential Privacy Integration

class DPBitDelta:
    """
    Differentially private BitDelta training
    """
    
    def train_with_privacy(
        self,
        base_model,
        user_data,
        epsilon=1.0,
        delta=1e-5
    ):
        # Use DP-SGD for training
        privacy_engine = PrivacyEngine()
        model, optimizer, data_loader = privacy_engine.make_private(
            module=base_model,
            optimizer=optimizer,
            data_loader=DataLoader(user_data),
            noise_multiplier=compute_noise(epsilon, delta),
            max_grad_norm=1.0
        )
        
        # Train with privacy
        for epoch in range(num_epochs):
            for batch in data_loader:
                loss = compute_loss(model, batch)
                loss.backward()
                optimizer.step()
                optimizer.zero_grad()
        
        # Get privacy spent
        epsilon_spent, delta_spent = privacy_engine.get_privacy_spent()
        
        # Compress to BitDelta
        bitdelta = compress_to_bitdelta(base_model, model)
        
        return bitdelta, PrivacyBudget(epsilon_spent, delta_spent)

On-Chain Integration

// Implements LP-303 (ILPRoyalties) and LP-107 (PersonaCredential)
contract BitDeltaRegistry {
    struct BitDelta {
        bytes32 baseModelHash;
        bytes32 deltaHash;
        string ownerLuxId;          // did:lux:122:0x... (LP-200)
        uint256 compressionRatio;   // e.g., 10 for 10×
        bool isPrivate;
        PrivacyBudget privacy;
        bytes32 receiptHash;        // LP-105 ComputeReceipt
    }
    
    struct DeltaSoup {
        bytes32 referenceAdapterHash;
        string[] contributorLuxIds;  // Array of did:lux identifiers
        uint256[] weights;            // Contribution weights for royalties (LP-106)
        uint256 version;
        bool active;
        RoyaltyMap royalties;         // LP-106 royalty distribution
    }
    
    mapping(string => BitDelta[]) public userDeltas;  // Keyed by Lux ID
    mapping(uint256 => DeltaSoup) public referenceSoups;
    
    event DeltaPublished(string userLuxId, bytes32 deltaHash, bool isPrivate);
    event SoupCreated(uint256 soupId, bytes32 adapterHash, string[] contributorLuxIds);
    
    function publishBitDelta(
        string calldata userLuxId,  // did:lux:122:0x...
        bytes32 baseModelHash,
        bytes32 deltaHash,
        uint256 compressionRatio,
        bool makePublic,
        bytes calldata teeAttestation,
        bytes calldata computeReceipt  // LP-105
    ) external {
        require(verifyTEE(teeAttestation), "Invalid TEE attestation");
        require(verifyLuxId(userLuxId, msg.sender), "Invalid Lux ID");
        
        BitDelta memory delta = BitDelta({
            baseModelHash: baseModelHash,
            deltaHash: deltaHash,
            ownerLuxId: userLuxId,
            compressionRatio: compressionRatio,
            isPrivate: !makePublic,
            privacy: extractPrivacyBudget(teeAttestation),
            receiptHash: keccak256(computeReceipt)
        });
        
        userDeltas[userLuxId].push(delta);
        emit DeltaPublished(userLuxId, deltaHash, !makePublic);
    }
    
    function createDeltaSoup(
        bytes32[] calldata deltaHashes,
        string[] calldata contributorLuxIds,  // did:lux identifiers
        uint256[] calldata weights,
        bytes calldata aggregationProof,
        RoyaltyMap calldata royaltyDistribution  // LP-106
    ) external returns (uint256 soupId) {
        require(verifyAggregation(aggregationProof), "Invalid aggregation");
        require(contributorLuxIds.length == weights.length, "Mismatched arrays");
        
        soupId = nextSoupId++;
        
        referenceSoups[soupId] = DeltaSoup({
            referenceAdapterHash: keccak256(abi.encode(deltaHashes)),
            contributorLuxIds: contributorLuxIds,
            weights: weights,
            version: block.timestamp,
            active: true,
            royalties: royaltyDistribution
        });
        
        emit SoupCreated(soupId, referenceSoups[soupId].referenceAdapterHash, contributorLuxIds);
    }
}

Serving Infrastructure

class BitDeltaServingEngine:
    """
    High-performance serving with PagedAttention + BitDelta
    """
    
    def __init__(self):
        self.base_model = load_base_model()
        self.delta_cache = LRUCache(max_size=10000)  # 10K users
        self.gpu_memory = GPUMemoryPool()
    
    def serve_request(self, user_lux_id: str, prompt):  # did:lux:122:0x...
        # Load user's BitDelta (compressed)
        delta = self.delta_cache.get(user_lux_id)
        if not delta:
            delta = load_from_storage(user_lux_id)
            self.delta_cache.put(user_lux_id, delta)
        
        # Efficient batched serving
        with self.gpu_memory.allocate(delta.memory_required()):
            # Fused kernel for base + BitDelta
            weights = fused_bitdelta_add(
                base=self.base_model.weights,
                signs=delta.signs,
                scales=delta.scales
            )
            
            # Use PagedAttention for KV cache
            output = paged_attention_generate(
                weights=weights,
                prompt=prompt,
                max_tokens=512
            )
        
        return output

Rationale

Why BitDelta?

Memory Efficiency: 10× reduction enables serving millions of users
Safety Preservation: Quantization constrains harmful adaptations
Fast Switching: Binary operations for rapid user context switching
Privacy: Compressed format reduces attack surface

Why DeltaSoup?

Community Intelligence: Aggregate improvements from all users
Byzantine Robustness: Trimmed mean resists poisoning attacks
Reproducibility: Single reference adapter vs ensemble overhead
Fair Attribution: On-chain royalties for contributors

Implementation Roadmap

Phase 1: BitDelta Core (Q1 2025)

Compression algorithm implementation
GPU-CC integration for secure training
Single-user serving infrastructure

Phase 2: DeltaSoup Alpha (Q2 2025)

Robust aggregation algorithms
Safety verification gates
Contributor tracking system

Phase 3: Production Scale (Q3 2025)

10K+ concurrent users
Automated soup creation
Royalty distribution

Phase 4: Open Ecosystem (Q4 2025)

Public delta marketplace
Cross-model transfer learning
Community governance

Security Considerations

TEE Attestation: All deltas must be created in secure enclaves
Privacy Budgets: Track and limit (ε,δ) per user
Backdoor Detection: Automated scanning of contributed deltas
Robust Aggregation: Byzantine fault tolerance up to 30% malicious

Testing

def test_bitdelta_compression():
    """Test 10× compression with <10% performance loss"""
    base = load_model("zoo-base-7b")
    finetuned = finetune(base, user_data)
    
    # Compress
    bitdelta = compress_to_bitdelta(base, finetuned)
    
    # Verify compression ratio
    original_size = calculate_size(finetuned - base)
    compressed_size = calculate_size(bitdelta)
    assert compressed_size <= original_size / 10
    
    # Verify performance
    original_score = evaluate(finetuned)
    compressed_score = evaluate(base + bitdelta)
    assert compressed_score >= 0.9 * original_score

def test_deltasoup_robustness():
    """Test Byzantine robustness of aggregation"""
    good_deltas = [create_good_delta() for _ in range(70)]
    bad_deltas = [create_poisoned_delta() for _ in range(30)]
    
    soup = create_deltasoup(good_deltas + bad_deltas)
    
    # Verify soup quality despite 30% poisoned
    assert evaluate(soup) >= evaluate(good_deltas[0])

Reference Implementation

Repository: zooai/bitdelta

Key Files:

/bitdelta/quantization.py - 1-bit weight quantization implementation
/bitdelta/deltasoup.py - DeltaSoup aggregation with Byzantine robustness
/bitdelta/model_swapping.py - Fast user model switching (< 10ms)
/storage/distributed_storage.py - Distributed delta weight storage
/aggregation/byzantine_defense.py - Krum and trimmed mean aggregation
/personalization/user_adapter.py - Per-user model adaptation engine
/benchmarks/memory_efficiency.py - Memory usage benchmarking
/benchmarks/safety_evaluation.py - Jailbreak resistance testing
/api/delta_api.ts - API for delta weight management
/sdk/python/ - Python SDK for BitDelta operations
/tests/poisoning_tests.py - Byzantine attack resistance tests
/tests/quality_tests.py - Model quality preservation tests
/docs/technical_spec.md - Detailed technical specification

Status: In Development (Beta Q2 2025)

Performance Characteristics:

Memory reduction: 10-20× vs. full weights
Model switching latency: < 10ms
Jailbreak resistance: ~60% improvement
Byzantine tolerance: Up to 30% poisoned deltas

Integration:

ZIP-3 Eco-1 model personalization
ZIP-6 User-owned model NFTs
ZIP-8 Avatar tutor customization
ZIP-9 Unified architecture

BitDelta + DeltaSoup - Personalized and Community AI