Layer 2 Rollup Framework

Abstract

This LP defines a comprehensive Layer 2 (L2) rollup framework for the Lux Network, enabling high-throughput execution environments for AI compute workloads (HIPs) and distributed training tasks (ZIPs). The framework supports both optimistic and zero-knowledge rollup architectures with specialized primitives for confidential compute using Trusted Execution Environments (TEEs), hardware security modules (HSMs), and cryptographic proof systems. It establishes standardized interfaces for rollup deployment, state management, and cross-layer communication while maintaining composability with existing Lux infrastructure.

Activation

Parameter	Value
Flag string	lp500-l2-rollup-framework
Default in code	false until block X
Deployment branch	v0.0.0-lp500
Roll‑out criteria	3+ rollup implementations
Back‑off plan	Gradual migration path

Motivation

The exponential growth of AI/ML workloads and distributed training requirements necessitates specialized execution environments that can:

1. Scale Horizontally: Support thousands of concurrent AI inference and training tasks
2. Preserve Privacy: Enable confidential compute for sensitive models and data
3. Reduce Costs: Amortize gas costs across batched operations
4. Maintain Security: Inherit L1 security guarantees through cryptographic proofs
5. Enable Specialization: Allow domain-specific optimizations for AI/ML workloads

Current L1 execution is insufficient for complex AI operations requiring gigaflops of compute and terabytes of data movement. This framework enables specialized L2s that can process AI workloads efficiently while settling to L1 for security.

Specification

Core Architecture

interface ILuxRollup {
    enum RollupType { Optimistic, ZKRollup, Hybrid, Sovereign }
    enum ExecutionMode { General, AICompute, DistributedTraining, Confidential }

    struct RollupConfig {
        RollupType rollupType;
        ExecutionMode executionMode;
        address sequencer;
        address proposer;
        address challenger;
        uint256 challengePeriod;
        uint256 minStake;
        bytes32 genesisStateRoot;
        bytes initCode;
    }

    struct BatchHeader {
        uint256 batchNumber;
        bytes32 parentBatchHash;
        bytes32 stateRoot;
        bytes32 transactionRoot;
        bytes32 receiptRoot;
        uint256 timestamp;
        uint256 l1BlockNumber;
        address sequencer;
        bytes signature;
    }

    // Core rollup functions
    function initialize(RollupConfig calldata config) external;
    function submitBatch(BatchHeader calldata header, bytes calldata transactions) external;
    function proposeStateRoot(uint256 batchNumber, bytes32 stateRoot, bytes calldata proof) external;
    function challengeStateRoot(uint256 batchNumber, bytes calldata fraudProof) external;
    function finalizeStateRoot(uint256 batchNumber) external;

    // Cross-layer communication
    function depositToL2(address token, uint256 amount, bytes calldata userData) external payable;
    function initiateWithdrawal(address token, uint256 amount, bytes calldata proof) external;
    function finalizeWithdrawal(uint256 withdrawalId, bytes calldata proof) external;

    // Events
    event BatchSubmitted(uint256 indexed batchNumber, bytes32 indexed batchHash);
    event StateRootProposed(uint256 indexed batchNumber, bytes32 stateRoot);
    event StateRootChallenged(uint256 indexed batchNumber, address challenger);
    event StateRootFinalized(uint256 indexed batchNumber, bytes32 stateRoot);
}

AI Compute Rollup Extensions

interface IAIComputeRollup is ILuxRollup {
    struct ComputeTask {
        bytes32 taskId;
        bytes32 modelHash;
        bytes inputDataHash;
        uint256 requiredFlops;
        uint256 maxGasPrice;
        address requester;
        TEEAttestation attestation;
    }

    struct TEEAttestation {
        bytes32 mrEnclave;       // Measurement of enclave code
        bytes32 mrSigner;        // Enclave signer identity
        bytes publicKey;          // TEE public key
        bytes quote;              // Remote attestation quote
        uint256 timestamp;
    }

    struct ComputeResult {
        bytes32 taskId;
        bytes32 outputHash;
        bytes zkProof;            // ZK proof of correct computation
        TEEAttestation attestation;
        uint256 gasUsed;
    }

    // AI-specific operations
    function submitComputeTask(ComputeTask calldata task) external returns (bytes32);
    function submitComputeResult(ComputeResult calldata result) external;
    function verifyTEEAttestation(TEEAttestation calldata attestation) external view returns (bool);
    function challengeComputation(bytes32 taskId, bytes calldata counterProof) external;

    // Model management
    function registerModel(bytes32 modelHash, bytes calldata modelMetadata) external;
    function updateModelWeights(bytes32 modelHash, bytes calldata deltaWeights, bytes calldata proof) external;

    // Events
    event ComputeTaskSubmitted(bytes32 indexed taskId, address indexed requester);
    event ComputeResultSubmitted(bytes32 indexed taskId, bytes32 outputHash);
    event ModelRegistered(bytes32 indexed modelHash, address indexed owner);
}

Distributed Training Rollup Extensions

interface IDistributedTrainingRollup is ILuxRollup {
    struct TrainingJob {
        bytes32 jobId;
        bytes32 modelArchitectureHash;
        bytes32 datasetHash;
        uint256 numEpochs;
        uint256 batchSize;
        uint256 learningRate;
        uint256 minWorkers;
        uint256 rewardPool;
    }

    struct WorkerContribution {
        address worker;
        bytes32 gradientHash;
        uint256 dataPointsProcessed;
        bytes32[] dataShardHashes;
        bytes aggregationProof;
    }

    struct FederatedUpdate {
        bytes32 jobId;
        uint256 round;
        WorkerContribution[] contributions;
        bytes32 aggregatedGradientHash;
        bytes convergenceProof;
    }

    // Training coordination
    function createTrainingJob(TrainingJob calldata job) external returns (bytes32);
    function joinTrainingJob(bytes32 jobId, bytes calldata workerAttestation) external;
    function submitGradientUpdate(bytes32 jobId, WorkerContribution calldata contribution) external;
    function aggregateGradients(bytes32 jobId, FederatedUpdate calldata update) external;

    // Secure aggregation
    function initSecureAggregation(bytes32 jobId, bytes calldata publicKeys) external;
    function submitEncryptedGradient(bytes32 jobId, bytes calldata encryptedGradient) external;
    function revealAggregationKey(bytes32 jobId, bytes calldata keyShare) external;

    // Incentive distribution
    function distributeRewards(bytes32 jobId, uint256 round) external;
    function slashMaliciousWorker(address worker, bytes calldata misbehaviorProof) external;

    // Events
    event TrainingJobCreated(bytes32 indexed jobId, uint256 rewardPool);
    event WorkerJoined(bytes32 indexed jobId, address indexed worker);
    event GradientSubmitted(bytes32 indexed jobId, address indexed worker, uint256 round);
    event ModelUpdated(bytes32 indexed jobId, uint256 round, bytes32 newWeightsHash);
}

State Transition Function

interface IStateTransition {
    struct StateTransition {
        bytes32 preStateRoot;
        bytes32 postStateRoot;
        bytes[] transactions;
        bytes[] receipts;
        bytes witness;        // Merkle proofs for accessed state
    }

    // State transition verification
    function verifyStateTransition(StateTransition calldata transition) external view returns (bool);
    function computeStateRoot(bytes[] calldata transactions) external pure returns (bytes32);
    function generateFraudProof(StateTransition calldata transition, uint256 txIndex) external view returns (bytes memory);

    // Merkle proof utilities
    function verifyInclusion(bytes32 root, bytes calldata proof, bytes calldata leaf) external pure returns (bool);
    function computeMerkleRoot(bytes[] calldata leaves) external pure returns (bytes32);
}

Cross-Layer Message Passing

interface IMessageBridge {
    struct L2Message {
        address sender;
        address target;
        uint256 value;
        uint256 gasLimit;
        bytes data;
        uint256 nonce;
    }

    struct MessageProof {
        bytes32 batchHash;
        bytes32 messageHash;
        bytes merkleProof;
        uint256 messageIndex;
    }

    // L1 -> L2 messaging
    function sendMessageToL2(address target, bytes calldata data) external payable returns (uint256);
    function relayMessageToL2(L2Message calldata message, bytes calldata signature) external;

    // L2 -> L1 messaging
    function sendMessageToL1(address target, bytes calldata data) external returns (bytes32);
    function relayMessageFromL2(L2Message calldata message, MessageProof calldata proof) external;

    // Message status
    function getMessageStatus(bytes32 messageHash) external view returns (uint8);
    function isMessageExecuted(bytes32 messageHash) external view returns (bool);
}

Rationale

Design Principles

1. Modularity: Separation of consensus, execution, and data availability layers following Buterin (2021) "The Limits to Blockchain Scalability"
2. Composability: Standardized interfaces enable rollup interoperability per Gudgeon et al. (2020) "SoK: Layer-Two Blockchain Protocols"
3. Security Inheritance: L2 inherits L1 security through cryptographic proofs (Teutsch & Reitwießner, 2019)
4. Execution Flexibility: Support for both general and specialized execution environments

TEE Integration Rationale

Trusted Execution Environments provide hardware-backed security guarantees essential for:

• Confidential AI Models: Protecting proprietary model weights and architectures
• Private Data Processing: GDPR-compliant data processing without exposure
• Verifiable Computation: Hardware attestation provides proof of correct execution

Following Intel SGX, AMD SEV, and ARM TrustZone specifications for maximum compatibility.

ZK Proof System Selection

The framework supports multiple proof systems based on use case requirements:

Proof System	Prover Time	Verifier Time	Proof Size	Use Case
Groth16	O(n log n)	O(1)	128 bytes	Small circuits
PLONK	O(n log n)	O(1)	380 bytes	General purpose
STARK	O(n log² n)	O(log² n)	~45 KB	Large computations
Bulletproofs	O(n log n)	O(n)	1-2 KB	Range proofs

Distributed Training Architecture

Following the framework proposed in Konečný et al. (2016) "Federated Learning: Strategies for Improving Communication Efficiency":

1. Secure Aggregation: Bonawitz et al. (2017) protocol for privacy-preserving gradient aggregation
2. Byzantine Fault Tolerance: Blanchard et al. (2017) "Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent"
3. Differential Privacy: Abadi et al. (2016) "Deep Learning with Differential Privacy"

Backwards Compatibility

This LP introduces new functionality without breaking existing contracts. Migration path:

1. Deploy rollup framework contracts
2. Existing dApps can opt-in to L2 execution
3. Maintain L1 fallback for critical operations
4. Gradual migration of compute-intensive workloads

Test Cases

Basic Rollup Operations

describe("L2 Rollup Framework", () => {
    it("should initialize rollup with correct parameters", async () => {
        const config = {
            rollupType: RollupType.ZKRollup,
            executionMode: ExecutionMode.AICompute,
            sequencer: sequencerAddress,
            challengePeriod: 7 * 24 * 3600, // 7 days
            minStake: ethers.parseEther("100")
        };
        await rollup.initialize(config);
        expect(await rollup.config()).to.deep.equal(config);
    });

    it("should submit and finalize batch", async () => {
        const batch = createBatch(transactions);
        await rollup.submitBatch(batch.header, batch.transactions);

        // Wait for challenge period
        await time.increase(challengePeriod);

        await rollup.finalizeStateRoot(batch.header.batchNumber);
        expect(await rollup.finalizedStateRoot()).to.equal(batch.header.stateRoot);
    });

    it("should handle fraud proof challenge", async () => {
        const invalidBatch = createInvalidBatch();
        await rollup.submitBatch(invalidBatch.header, invalidBatch.transactions);

        const fraudProof = generateFraudProof(invalidBatch);
        await rollup.challengeStateRoot(invalidBatch.header.batchNumber, fraudProof);

        expect(await rollup.getBatchStatus(invalidBatch.header.batchNumber))
            .to.equal(BatchStatus.Rejected);
    });
});

AI Compute Tests

describe("AI Compute Rollup", () => {
    it("should verify TEE attestation", async () => {
        const attestation = {
            mrEnclave: "0x" + "a".repeat(64),
            mrSigner: "0x" + "b".repeat(64),
            publicKey: teePublicKey,
            quote: generateQuote(mrEnclave, mrSigner),
            timestamp: Date.now()
        };

        expect(await rollup.verifyTEEAttestation(attestation)).to.be.true;
    });

    it("should execute compute task with proof", async () => {
        const task = {
            taskId: keccak256("task1"),
            modelHash: keccak256("gpt-model"),
            inputDataHash: keccak256("input"),
            requiredFlops: ethers.parseUnits("100", 12),
            maxGasPrice: ethers.parseUnits("100", "gwei")
        };

        await rollup.submitComputeTask(task);

        const result = {
            taskId: task.taskId,
            outputHash: keccak256("output"),
            zkProof: generateComputeProof(task, output),
            attestation: teeAttestation,
            gasUsed: 1000000
        };

        await rollup.submitComputeResult(result);
        expect(await rollup.getTaskResult(task.taskId)).to.equal(result.outputHash);
    });
});

Distributed Training Tests

describe("Distributed Training Rollup", () => {
    it("should coordinate federated learning round", async () => {
        const job = {
            jobId: keccak256("training1"),
            modelArchitectureHash: keccak256("transformer"),
            datasetHash: keccak256("dataset"),
            numEpochs: 10,
            batchSize: 32,
            learningRate: ethers.parseUnits("0.001", 18),
            minWorkers: 5,
            rewardPool: ethers.parseEther("1000")
        };

        await rollup.createTrainingJob(job);

        // Workers join
        for(let i = 0; i < 5; i++) {
            await rollup.connect(workers[i]).joinTrainingJob(job.jobId, attestations[i]);
        }

        // Submit gradients
        const contributions = await Promise.all(workers.map(async (worker, i) => ({
            worker: worker.address,
            gradientHash: keccak256(`gradient${i}`),
            dataPointsProcessed: 1000,
            dataShardHashes: [keccak256(`shard${i}`)],
            aggregationProof: generateAggregationProof(i)
        })));

        const update = {
            jobId: job.jobId,
            round: 1,
            contributions,
            aggregatedGradientHash: keccak256("aggregated"),
            convergenceProof: generateConvergenceProof()
        };

        await rollup.aggregateGradients(job.jobId, update);

        // Verify rewards distributed
        await rollup.distributeRewards(job.jobId, 1);
        for(let worker of workers) {
            expect(await token.balanceOf(worker.address)).to.be.gt(0);
        }
    });

    it("should implement secure aggregation", async () => {
        const jobId = keccak256("secure-training");
        const publicKeys = workers.map(w => generatePublicKey(w));

        await rollup.initSecureAggregation(jobId, publicKeys);

        // Each worker submits encrypted gradient
        for(let i = 0; i < workers.length; i++) {
            const encryptedGradient = encrypt(gradients[i], publicKeys);
            await rollup.connect(workers[i]).submitEncryptedGradient(jobId, encryptedGradient);
        }

        // Reveal aggregation keys
        for(let i = 0; i < workers.length; i++) {
            const keyShare = generateKeyShare(workers[i], i);
            await rollup.connect(workers[i]).revealAggregationKey(jobId, keyShare);
        }

        // Verify aggregated result
        const aggregatedGradient = await rollup.getAggregatedGradient(jobId, 1);
        expect(aggregatedGradient).to.not.be.null;
    });
});

Reference Implementation

Reference implementations available at:

• https://github.com/luxfi/rollup-framework
• https://github.com/hanzoai/hip-rollup
• https://github.com/zooprotocol/zip-rollup

Key components:

• contracts/RollupCore.sol: Core rollup logic
• contracts/AIComputeRollup.sol: AI-specific extensions
• contracts/DistributedTrainingRollup.sol: Training coordination
• circuits/: ZK circuits for proof generation
• tee/: TEE attestation and verification

Security Considerations

Consensus Security

1. Data Availability: Ensure all transaction data is available on L1 or dedicated DA layer (Celestia, EigenDA)
2. Sequencer Centralization: Implement decentralized sequencer selection via stake-weighted random selection
3. MEV Resistance: Use threshold encryption for transaction ordering (Fair Sequencing Services)

Cryptographic Security

1. Proof System Security:

   • Regular ceremony updates for trusted setup (Powers of Tau)
   • Circuit audits by specialized firms (Trail of Bits, Least Authority)
   • Formal verification of critical circuits (Certora, Runtime Verification)

2. TEE Security:

   • Side-channel attack mitigation (power analysis, timing attacks)
   • Regular firmware updates and patches
   • Multi-TEE redundancy for critical operations

Economic Security

1. Stake Requirements: Minimum stake of 100,000 LUX for sequencers
2. Challenge Bonds: 10% of sequencer stake required for challenges
3. Slashing Conditions: Malicious behavior results in 100% stake slashing
4. Reward Distribution: 50% to sequencers, 30% to provers, 20% to validators

AI-Specific Security

1. Model Poisoning: Detect via outlier detection and gradient clipping
2. Data Privacy: Implement differential privacy with ε < 1.0
3. Sybil Attacks: Require proof-of-compute for worker participation
4. Free-Riding: Verify actual computation via random challenges

Economic Impact

Cost Reduction

• 100-1000x reduction in per-transaction costs
• Amortized proof verification across batches
• Efficient data compression techniques

New Revenue Streams

• Sequencer fees: 0.1-1% of transaction value
• Compute marketplace: 10-20% platform fee
• Training coordination: 5% of reward pools

Token Utility

• Staking for sequencer/validator roles
• Payment for compute resources
• Governance of rollup parameters

Open Questions

1. Optimal Batch Size: Trade-off between latency and cost efficiency
2. Cross-Rollup Communication: Standardization of bridge protocols
3. Quantum Resistance: Migration path to post-quantum cryptography
4. Regulatory Compliance: KYC/AML integration without compromising privacy

References

1. Buterin, V. (2021). "The Limits to Blockchain Scalability." Vitalik.ca
2. Gudgeon, L., et al. (2020). "SoK: Layer-Two Blockchain Protocols." Financial Cryptography
3. Teutsch, J., & Reitwießner, C. (2019). "A Scalable Verification Solution for Blockchains"
4. Konečný, J., et al. (2016). "Federated Learning: Strategies for Improving Communication Efficiency"
5. Bonawitz, K., et al. (2017). "Practical Secure Aggregation for Privacy-Preserving Machine Learning"
6. Blanchard, P., et al. (2017). "Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent"
7. Abadi, M., et al. (2016). "Deep Learning with Differential Privacy"
8. Intel (2023). "Intel SGX Developer Guide"
9. AMD (2023). "SEV Secure Encrypted Virtualization API"
10. Groth, J. (2016). "On the Size of Pairing-Based Non-Interactive Arguments"

Implementation

Status: Specification stage - implementation planned for future release

Planned Locations:

• Rollup core: ~/work/lux/rollup/ (to be created)
• Contracts: ~/work/lux/standard/src/rollup/ (EVM precompiles)
• AI compute: ~/work/lux/ai-compute/ (integration)
• Testing: ~/work/lux/netrunner/rollup/ (test networks)

Design Pattern: Will follow existing patterns from:

• ~/work/lux/node/vms/proposervm/ - Block format
• ~/work/lux/consensus/ - State management
• ~/work/lux/database/ - Merkle tree operations

Expected Timeline:

• Q1 2026: Core implementation and testing
• Q2 2026: AI workload integration
• Q3 2026: Cross-rollup communication
• Q4 2026: Production readiness

Integration with Existing Systems:

• Uses existing Lux P-Chain validators
• Compatible with Q-Chain for post-quantum security
• Integrates with Warp cross-chain messaging
• Leverages existing consensus engines (BFT, Chain, DAG)

Copyright

Copyright and related rights waived via CC0.