Technical Implementation

  1. Blockchain Integration

  2. struct NetworkNode {
        uint256 computePower;
        bytes32 nodeIdentifier;
        address[] connections;
        uint256 stakingAmount;
    }
    
    struct ModelMetadata {
        bytes32 modelHash;
        uint256 version;
        uint256 accuracy;
        mapping(address => uint256) contributions;
    }
    1. Neural Network Architecture

    The system implements a modified transformer architecture optimized for distributed computing:

    class DistributedAttention(nn.Module):
        def __init__(self, dim, heads=8):
            super().__init__()
            self.heads = heads
            self.scale = dim ** -0.5
            self.to_qkv = nn.Linear(dim, dim * 3, bias=False)
            
        def forward(self, x):
            q, k, v = self.to_qkv(x).chunk(3, dim=-1)
            dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
            attn = dots.softmax(dim=-1)
            return torch.matmul(attn, v)
    1. Training Protocol

    The distributed training process follows these key principles:

    • Federated learning across network nodes

    • Gradient aggregation with verification

    • Model versioning and consensus

    • Automated performance optimization

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