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