如何實作自定義編碼TensorflowFederated

我創建了一個自定義編碼器/解碼器，如下所示：

import tensorflow as tf

from tensorflow_model_optimization.python.core.internal import tensor_encoding as te


# noinspection PyUnresolvedReferences
class SparseTernaryCompressionEncodingStage(te.core.EncodingStageInterface):
    AVERAGE = 'average'
    NEGATIVES = 'negatives'
    POSITIVES = 'positives'
    TESTING = 'testing'
    NEW_SHAPE = 'new_shape'
    ORIGINAL_SHAPE = 'original_shape'

    def name(self):
        pass

    def compressible_tensors_keys(self):
        pass

    def commutes_with_sum(self):
        pass

    def decode_needs_input_shape(self):
        pass

    def get_params(self):
        pass

    def encode(self, original_tensor, encode_params):
        original_shape = tf.shape(original_tensor)
        tensor = tf.reshape(original_tensor, [-1])
        sparsification_rate = int(len(tensor) / 100 * 1)
        new_shape = tensor.get_shape().as_list()
        if sparsification_rate == 0:
            sparsification_rate = 1
        mask = tf.cast(tf.abs(tensor) >= tf.math.top_k(tf.abs(tensor), sparsification_rate)[0][-1], tf.float32)
        inv_mask = tf.cast(tf.abs(tensor) < tf.math.top_k(tf.abs(tensor), sparsification_rate)[0][-1], tf.float32)
        tensor_masked = tf.multiply(tensor, mask)
        average = tf.reduce_sum(tf.abs(tensor_masked)) / sparsification_rate
        compressed_tensor = tf.add(tf.multiply(average, mask) * tf.sign(tensor), tf.multiply(tensor_masked, inv_mask))
        negatives = tf.where(compressed_tensor < 0)
        positives = tf.where(compressed_tensor > 0)

        encoded_x = {self.AVERAGE: average, self.NEGATIVES: negatives, self.POSITIVES: positives,
                     self.NEW_SHAPE: new_shape, self.ORIGINAL_SHAPE: original_shape}

        return encoded_x

    def decode(self, encoded_tensors, decode_params, num_summands=None, shape=None):
        decompressed_tensor = tf.zeros(self.NEW_SHAPE, tf.float32)
        average_values_negative = tf.fill([len(self.NEGATIVES), ], -self.AVERAGE)
        average_values_positive = tf.fill([len(self.POSITIVES), ], self.AVERAGE)
        decompressed_tensor = tf.tensor_scatter_nd_update(decompressed_tensor, self.NEGATIVES, average_values_negative)
        decompressed_tensor = tf.tensor_scatter_nd_update(decompressed_tensor, self.POSITIVES, average_values_positive)
        decompressed_tensor = tf.reshape(decompressed_tensor, self.ORIGINAL_SHAPE)
        return decompressed_tensor

現在，我想使用 encode 函式對客戶端發送到服務器的所有權重進行編碼，并在服務器上使用 decode 函式來獲取所有權重。基本上，不是將所有權重從客戶端發送到服務器，我只想發送一些必要的資訊，讓我能夠僅從 5 個資訊中創建權重。

問題是我不明白如何在嘗試之前告訴客戶端使用此編碼器發送資訊并到服務器使用解碼器： round_model_delta = tff.federated_mean(client_outputs.weights_delta, weight=weight_denom)

我使用Tensorflow Federated simple_fedavg作為基本專案。

uj5u.com熱心網友回復：

如果您只想修改聚合，您可以更輕松地使用tff.learningAPI 和您所擁有的，使用tff.aggregators物件引數化聚合。例如：

te.core.EncoderComposer(te.testing.PlusOneOverNEncodingStage()).make()

def encoder_fn(value_spec):
  return te.encoders.as_gather_encoder(
     te.core.EncoderComposer(SparseTernaryCompressionEncodingStage()).make(),
     value_spec)

tff.learning.build_federated_averaging_process(
    ...,  # Other args.
    model_update_aggregation_factory=tff.aggregators.EncodedSumFactory(
        encoder_fn))

您可能還會發現這些教程很有幫助：

• https://www.tensorflow.org/federated/tutorials/tuning_recommended_aggregators
• https://www.tensorflow.org/federated/tutorials/custom_aggregators

標籤：Python 张量流 张量流联合

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