import torch
from transformers import BertModel, BertTokenizer

class EdgeMultiModalEngine(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.text_encoder = BertModel.from_pretrained("bert-base-uncased", 
                                                     output_attentions=True)
        self.image_encoder = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14')
        self.fusion_layer = torch.nn.Transformer(d_model=768, nhead=12)

    def forward(self, text_input, image_input):
        # 文本特征提取
        text_features = self.text_encoder(**text_input).last_hidden_state

        # 图像特征提取
        image_features = self.image_encoder(image_input)

        # 跨模态注意力融合
        combined = self.fusion_layer(text_features, image_features)
        return torch.mean(combined, dim=1)

from torch_federated import FederatedAveraging

class DynamicFederatedOptimizer:
    def __init__(self):
        self.aggregator = FederatedAveraging(
            aggregation_freq=10,
            privacy_budget=0.1,
            knowledge_distillation=True
        )

    def update_model(self, local_updates):
        # 时间衰减加权
        weights = [1/(1+np.exp(-t)) for t in update_timestamps]

        # 差分隐私处理
        noisy_updates = [self._add_dp_noise(u, w) for u,w in zip(local_updates, weights)]

        # 知识蒸馏
        distilled_model = self._distill(noisy_updates)
        return distilled_model

    def _add_dp_noise(self, update, weight):
        noise_scale = weight * self.epsilon / 100
        return update + torch.normal(0, noise_scale, size=update.shape)

from sympy import Matrix
from sympy.crypto.crypto import encipher_Hill

def secure_data_transmission(data):
    # 基于Hill密码的加密示例
    key = Matrix([[1, 2], [3, 5]])
    encrypted = encipher_Hill(data, key)

    # 结合差分隐私
    noise = np.random.laplace(0, 1/epsilon, len(encrypted))
    return encrypted + noise