多模态特征与联邦隐私：主权认知网络

在群体智能协作时代，多租户（Tenant）的智能体需要进行联合记忆提纯。然而，情节记忆极易泄露 PII（个人敏感信息），并且网络中存在恶性噪声对共有质心进行毒化的风险。VecminDB 引入了 主权联邦认知网络 (Sovereign Federation) 解决这一问题。

1. 实体敏感网关 (Entity-Sensitive Gating)

在任何分片向量被吸纳到联邦池之前，必须通过部署在本地的网关进行一票否决拦截。网关内置了 5 个高精度正则表达式：

  1. Email 电子邮件：(?i)[a-z0-9._%+-]+@[a-z0-9.-]+\.[a-z]{2,}
  2. 美国社会保障号 (US SSN)：\b\d{3}-\d{2}-\d{4}\b
  3. 信用卡号 (Credit Card)：\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b
  4. 中国居民身份证 (18位)：\b\d{17}[\dXx]\b
  5. IPv4 地址：\b(?:[0-9]{1,3}\.){3}[0-9]{1,3}\b

当本地任务扫描待蒸馏向量时，网关会检查元数据（VectorMetadata）的 Key 和 String 类型的 Value。一旦匹配到敏感信息，网关将执行 Sovereignty Border Guard 熔断拦截，拒绝其输出到联邦公共空间。

2. 防对抗污染：异常值裁剪哨兵 (Outlier Pruning Sentinel)

为防止对抗性噪声毒化公共语义空间，系统在联邦池聚集候选向量后，利用 3-Sigma 统计分步规则评估向量相似度的偏离程度。

2.1 自适应余弦相似度截断算法

计算每个候选向量与全局均值中心点 $\mu_{global}$ 的余弦相似度，并求解相似度期望值 $Mean_s$ 与标准差 $Std_s$。自适应动态门槛值计算公式为：

Threshold = max( Mean_s - 3 * Std_s , 0.7 )

任何相似度小于该阈值的向量将被哨兵判定为**离群污染源（Outlier）**并物理剔除，不得流入差分隐私降维管线。

3. 动态信誉自适应模型 (Dynamic Trust Scores)

系统为每个 Tenant 动态分配信誉权重 $T \in [0.05, 1.0]$。在每个蒸馏周期结束后，系统根据该 Tenant 的候选向量在哨兵阶段的通过率（Pass Rate），使用**指数移动平均 (EMA)** 滤波器进行信誉修正：

T_new = 0.2 * PassRate + 0.8 * T_old

对算得的 $T_{new}$ 强制施加硬锁定约束：

T_new = Clamp( T_new, 0.05, 1.0 )

当节点持续提交漂移向量时，其信誉分会迅速滑落至 0.05 物理极限，其对公共质心生成的影响将被压制为零。

4. 差分隐私联邦主成分提纯 (DP-Federated PCA)

健康向量与信誉权重 $T$ 最终进入 DP-Federated PCA 降维管线，在加权自协方差矩阵累加过程中引入 Laplace 差分隐私噪声防范逆向推导，并叠加第一主成分 $P_0$ 10% 的方向偏置来解算冷凝的联盟质心：

v_centroid = Mean_global + P_0 * 0.1

联盟质心将被持久化至只读共享层，反哺给多租户智能体实现无隐私的经验承袭。

Multi-Modal Features & Federated Privacy: Sovereign Network

In swarm intelligence, agents across tenants need to synthesize collective memories. However, episodic memories expose PII (Personally Identifiable Information) and present adversarial vector pollution risks. VecminDB introduces the **Sovereign Federation Cognitive Network** to resolve this threat.

1. Entity-Sensitive Gating

Before any local vector crosses the sovereignty boundary into the federated pool, the local gateway enforces static regex checks. The gateway contains 5 high-precision regular expressions:

  1. Email Address: (?i)[a-z0-9._%+-]+@[a-z0-9.-]+\.[a-z]{2,}
  2. US SSN: \b\d{3}-\d{2}-\d{4}\b
  3. Credit Card Numbers: \b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b
  4. Chinese ID Card: \b\d{17}[\dXx]\b
  5. IP Address (IPv4): \b(?:[0-9]{1,3}\.){3}[0-9]{1,3}\b

If any key or string value in VectorMetadata matches the patterns, the gateway triggers a **Sovereignty Border Guard** veto, keeping the vector inside the local private domain.

2. Outlier Pruning Sentinel

To block adversarial vector poisonings, the sentinel gauges cosine similarities of candidates against the global mean $\mu_{global}$, calculating the mean $Mean_s$ and standard deviation $Std_s$ of similarity distributions:

Threshold = max( Mean_s - 3 * Std_s , 0.7 )

Any vector falling below this threshold is treated as an **Outlier** and pruned from the PCA pipeline.

3. Dynamic Trust Scores

A reliability weight $T \in [0.05, 1.0]$ is computed for each tenant. After each cycle, the system adjusts $T$ using an **Exponential Moving Average (EMA)** filter based on the sentinel pass rate:

T_new = 0.2 * PassRate + 0.8 * T_old

The updated trust score is strictly clamped to preserve the trust boundaries:

T_new = Clamp( T_new, 0.05, 1.0 )

4. DP-Federated PCA Distillation

Aggregated local covariance matrices are protected by Laplace noise to satisfy differential privacy $\epsilon$. The finalized alliance centroid is calculated with a 10% principal component directional bias:

v_centroid = Mean_global + P_0 * 0.1

The resulting centroid is stored in the read-only shared layer, feeding collective intelligence back to the tenants securely.