┌─────────────────────────────────────────────────────────────┐
│  业务应用层：会议SDK集成、降噪开关、效果监控看板              │
├─────────────────────────────────────────────────────────────┤
│  联邦协调层：任务调度、客户端选取、安全聚合、模型分发、版本管理 │
├─────────────────────────────────────────────────────────────┤
│  端侧训练层：本地数据采集增强、轻量化模型训练、差分隐私加噪   │
├─────────────────────────────────────────────────────────────┤
│  基础设施层：TLS 1.3通信通道、TEE可信执行环境、密钥管理服务(KMS)│
└─────────────────────────────────────────────────────────────┘

# 伪代码：端侧模型定义 (PyTorch风格)
class NoiseClassifier(nn.Module):
    def __init__(self, num_classes=18, width_mult=0.75):
        super().__init__()
        self.backbone = MobileNetV3_Small(width_mult=width_mult)  # ~1.2M params
        self.attention = SEBlock(576)  # 通道注意力增强频域特征
        self.head = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Flatten(),
            nn.Dropout(0.2),
            nn.Linear(576, num_classes)
        )
        # 知识蒸馏：教师模型为ResNet-50 (Top-1 Acc 94.2% on AudioSet)
        self.register_buffer('teacher_logits', None)  

    def forward(self, x):
        feat = self.backbone(x)
        feat = self.attention(feat)
        return self.head(feat)

min_{w_k} F_k(w_k) + frac{mu}{2} | w_k - w_g |^2_2

w_g = sum_{k in S_t} frac{n_k cdot text{Acc}_k^{val}}{sum n_j cdot text{Acc}_j^{val}} cdot w_k

// Android端调度伪代码
object FLScheduler {
    fun shouldTrainNow(context: Context): Boolean {
        return isCharging() 
            && isWifiConnected() 
            && !isInMeeting() 
            && getBatteryLevel() > 30%
            && getAvailableStorage() > 500MB
            && getThermalState() != THERMAL_STATUS_SEVERE
    }
    
    fun scheduleNextRound(lastTrainTime: Long) {
        val delay = when {
            lastTrainTime < 24h -> 6h   // 冷启动加速收敛
            lastTrainTime < 7d  -> 24h  // 稳定期
            else -> 72h               // 维护期
        }
        WorkManager.enqueueUniquePeriodicWork(
            "FL_TRAIN", ExistingPeriodicWorkPolicy.KEEP,
            PeriodicWorkRequestBuilder<FLTrainWorker>(delay, TimeUnit.HOURS).build()
        )
    }
}

graph LR
    A[原始音频流] --> B(VAD语音活动检测)
    B --> C{静音段/非语音段}
    C --> D[教师模型推理<br/>MobileNetV3-Large + SpecAugment]
    D --> E[伪标签生成]
    E --> F[一致性校验器]
    F -->|连续N帧类别一致<br/>置信度>τ| G[高质量样本入库]
    F -->|置信度低/类别跳变| H[不确定性池]
    H --> I[主动学习采样<br/>上传嵌入向量聚类]
    I --> J[服务端人工复核/半监督训练]
    J --> K[教师模型迭代更新]
    K --> D

# 伪代码：基于加法秘密分享的 KS 统计量计算
# 客户端侧
local_ecdf = compute_ecdf(local_features)  # 经验累积分布函数
shares = additive_secret_share(local_ecdf, parties=[Server, Auditor])
send_shares(shares)

# 服务端侧 (聚合后重构全局 ECDF)
global_ecdf = reconstruct(shares_from_all_clients)
ks_stat, p_value = ks_2samp(global_ecdf, reference_ecdf)
if p_value < 0.01:
    trigger_alert("Feature Drift Detected: p={:.4f}".format(p_value))
    # 触发：全局模型回滚 + 重标注流程启动 + 客户端采样率调整

def robust_aggregate(client_updates: List[Tensor], byzantine_ratio: float = 0.2):
    """
    client_updates: List of (delta_weights, client_id, val_acc)
    """
    # 1. 预过滤：剔除范数异常更新 (L2 norm > 3 * Median)
    norms = [u[0].norm().item() for u in client_updates]
    median_norm = torch.median(torch.tensor(norms))
    filtered = [u for u in client_updates if u[0].norm() < 3 * median_norm]
    
    # 2. 计算余弦相似度矩阵
    cos_sim = cosine_similarity_matrix([u[0] for u in filtered])
    
    # 3. Krum 评分：选择与邻居最一致的更新
    k = max(1, int(len(filtered) * (1 - byzantine_ratio)))
    krum_scores = []
    for i in range(len(filtered)):
        dists = sorted([1 - cos_sim[i][j] for j in range(len(filtered)) if i != j])
        krum_scores.append(sum(dists[:k]))
    
    # 4. Trimmed Mean 融合 Top-K Krum 得分客户端
    top_k_indices = torch.topk(torch.tensor(krum_scores), k, largest=False).indices
    selected_updates = [filtered[i][0] for i in top_k_indices]
    
    # 5. 坐标级修剪均值 (去除每个维度最大/最小 20%)
    stacked = torch.stack(selected_updates)
    trimmed = torch.mean(torch.sort(stacked, dim=0).values[
        int(0.2*len(selected_updates)) : int(0.8*len(selected_updates))
    ], dim=0)
    
    return trimmed

min_{theta} mathcal{L}_{CE} + lambda cdot mathbb{E}_{x sim D} left[ | nabla_x mathcal{L}_{CE} |_2^2 right] + gamma cdot text{DP-Noise}

# PyTorch FX Graph Mode QAT 配置关键点
from torch.ao.quantization import QConfigMapping, get_default_qat_qconfig
from torch.ao.quantization.quantize_fx import prepare_qat_fx, convert_fx

qconfig_mapping = QConfigMapping().set_global(
    get_default_qat_qconfig('qnnpack')  # 移动端目标后端
).set_object_type(nn.Conv2d, 
    # 卷积权重对称量化，激活非对称
    qconfig=torch.ao.quantization.QConfig(
        activation=torch.ao.quantization.observer.MovingAverageMinMaxObserver.with_args(
            dtype=torch.quint8, qscheme=torch.per_tensor_affine),
        weight=torch.ao.quantization.observer.PerChannelMinMaxObserver.with_args(
            dtype=torch.qint8, qscheme=torch.per_channel_symmetric)
    )
)

# 准备 QAT 模型
model_prepared = prepare_qat_fx(model_fp32.train(), qconfig_mapping, example_inputs)

# 训练微调 (关键：BN folding 需在转换前完成)
for epoch in range(qat_epochs):
    train_one_epoch(model_prepared)
    # 定期冻结 BN 统计量
    if epoch > qat_epochs // 2:
        model_prepared.apply(torch.ao.quantization.disable_observer)
        model_prepared.apply(torch.nn.intrinsic.qat.freeze_bn_stats)

# 转换部署模型
model_int8 = convert_fx(model_prepared.eval())

# .gitlab/ci/dpia_stage.yml
dpia_evidence_generation:
  stage: compliance
  image: python:3.10-slim
  script:
    - pip install dpia-toolkit
    # 1. 数据流图自动生成 (从架构 DSL 解析)
    - dpia-gen dataflow --arch=arch.yaml --output=dpia/dataflow.svg
    # 2. 隐私风险矩阵计算 (基于代码扫描规则)
    - dpia-gen risk --code-path=./src --rules=gdpr,pipl --output=dpia/risk_matrix.csv
    # 3. 技术措施有效性验证 (自动化测试报告)
    - dpia-gen tech-measure --test-report=test_results/junit.xml --output=dpia/tech_measures.md
    # 4. 生成最终 DPIA 报告 (PDF + 机器可读 JSON)
    - dpia-gen report --template=dpia_template.docx --output=artifacts/DPIA_Report_v${CI_COMMIT_SHORT_SHA}.pdf
  artifacts:
    paths:
      - artifacts/DPIA_Report_*.pdf
      - dpia/
    expire_in: 1 year
  only:
    - tags  # 仅发版触发

// 伪代码：联邦聚合前的合规闸口检查
func (s *FLCoordinator) PreAggregateCheck(round int, clientUpdates []ClientUpdate) error {
    // 1. 校验客户端归属地域
    for _, u := range clientUpdates {
        region := s.geoIP.Lookup(u.ClientIP)
        if region == "CN" && s.aggregatorRegion != "CN" {
            // 中国用户梯度不得发往海外聚合节点
            return ErrCrossBorderTransferForbidden
        }
    }
    
    // 2. 校验模型更新是否包含可识别特征 (简易启发式)
    for _, u := range clientUpdates {
        if entropy(u.DeltaWeights) < THRESHOLD_ENTROPY {
            // 低熵更新可能包含记忆化样本特征
            log.Warn("Low entropy update detected", "client", u.ClientID)
            // 可选：拒收或强制加大 LDP 噪声
        }
    }
    
    // 3. 记录审计日志 (WORM 存储)
    s.auditLog.Write(AuditEntry{
        Round: round,
        ClientCount: len(clientUpdates),
        Regions: uniqueRegions(clientUpdates),
        Hash: hashUpdates(clientUpdates),
        Timestamp: time.Now(),
    })
    return nil
}

C_i = left( Q_i^{alpha} times D_i^{beta} times S_i^{gamma} times T_i^{delta} right)^{frac{1}{alpha+beta+gamma+delta}}