aitbc/apps/zk-circuits/ml_training_verification.circom

pragma circom 2.0.0;

include "node_modules/circomlib/circuits/poseidon.circom";
include "node_modules/circomlib/circuits/comparators.circom";

/*
 * Simplified ML Training Verification Circuit
 *
 * Basic proof of gradient descent training without complex hashing
 */

template SimpleTrainingVerification(PARAM_COUNT, EPOCHS) {
    signal input initial_parameters[PARAM_COUNT];
    signal input learning_rate;

    signal output final_parameters[PARAM_COUNT];
    signal output training_complete;

    // Input validation constraints
    // Learning rate should be positive and reasonable (0 < lr < 1)
    component lt1 = LessThan(252);
    component gt0 = GreaterThan(252);
    
    // Ensure learning_rate < 1
    lt1.in[0] <== learning_rate;
    lt1.in[1] <== 1;
    lt1.out === 1;
    
    // Ensure learning_rate > 0
    gt0.in[0] <== learning_rate;
    gt0.in[1] <== 0;
    gt0.out === 1;

    // Simulate simple training epochs
    signal current_parameters[EPOCHS + 1][PARAM_COUNT];

    // Initialize with initial parameters
    for (var i = 0; i < PARAM_COUNT; i++) {
        current_parameters[0][i] <== initial_parameters[i];
    }

    // Simple training: gradient descent simulation
    for (var e = 0; e < EPOCHS; e++) {
        for (var i = 0; i < PARAM_COUNT; i++) {
            // Simplified gradient descent: param = param - learning_rate * gradient_constant
            // Using constant gradient of 0.1 for demonstration
            current_parameters[e + 1][i] <== current_parameters[e][i] - learning_rate * 1;
        }
    }

    // Output final parameters
    for (var i = 0; i < PARAM_COUNT; i++) {
        final_parameters[i] <== current_parameters[EPOCHS][i];
    }

    // Training completion constraint
    training_complete <== 1;
}

component main = SimpleTrainingVerification(4, 3);