Create fedpass.py

fedlearner/privacy/splitnn/fedpass.py

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+import tensorflow.compat.v1 as tf
+
+
+def scale_transform(s_scalekey):
+   """ 对密钥应用变换并计算缩放因子 """
+   _, s_c = tf.shape(s_scalekey)[0], tf.shape(s_scalekey)[1]
+   s_scale = tf.reduce_mean(s_scalekey, axis=0)
+   s_scale = tf.reshape(s_scale, [1, s_c])
+   return s_scale
+
+def fedpass(hidden_feature, x, mean, scale):
+   # hidden_feature: 中间层维度
+   # x: 输入数据
+   # mean, scale: 随机密钥的均值和方差
+
+   # 定义层
+   dense = tf.keras.layers.Dense(hidden_feature, use_bias=False, activation=None)
+   encode = tf.keras.layers.Dense(hidden_feature // 4, use_bias=False, activation=None)
+   decode = tf.keras.layers.Dense(hidden_feature, use_bias=False, activation=None)
+
+   # 初始化随机变量
+   newshape = tf.shape(x)
+   skey = tf.random.normal(newshape, mean=mean, stddev=scale, dtype=x.dtype)
+   bkey = tf.random.normal(newshape, mean=mean, stddev=scale, dtype=x.dtype)
+   # 应用层和计算缩放因子
+   s_scalekey = dense(skey)
+   b_scalekey = dense(bkey)
+
+
+   s_scale = scale_transform(s_scalekey)
+   b_scale = scale_transform(b_scalekey)
+
+   s_scale = tf.reshape(decode(tf.nn.leaky_relu(encode(s_scale))), [1, hidden_feature])
+   b_scale = tf.reshape(decode(tf.nn.leaky_relu(encode(b_scale))), [1, hidden_feature])
+   x = dense(x)
+   x = tf.tanh(s_scale) * x + tf.tanh(b_scale)
+   return x