Technical Implementation
Blockchain Integration
struct NetworkNode { uint256 computePower; bytes32 nodeIdentifier; address[] connections; uint256 stakingAmount; } struct ModelMetadata { bytes32 modelHash; uint256 version; uint256 accuracy; mapping(address => uint256) contributions; }
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)
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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