PRIVACY-SENSITIVE NEURAL NETWORK TRAINING USING DATA AUGMENTATION
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for privacy-sensitive training of a neural network. In one aspect, a method includes training a set of neural network parameters of the neural network on a set of training data over multiple training iterations to optimize an objective function. Each training iteration includes: sampling a batch of network inputs from the set of training data; determining a clipped gradient for each network input in the batch of network inputs; and updating the neural network parameters using the clipped gradients for the network inputs in the batch of network inputs.
1 . A method performed by one or more computers for privacy-sensitive training of a neural network, the method comprising:
training a set of neural network parameters of the neural network on a set of training data over a plurality of training iterations to optimize an objective function, comprising, at each training iteration:
sampling a plurality of network inputs from the set of training data;
determining a clipped gradient for each network input of the plurality of network inputs, comprising, for each network input of the plurality of network inputs:
generating a plurality of augmented versions of the network input, wherein each augmented version of the network input results from applying a respective augmentation transformation to the network input;
determining, for each of the plurality of augmented versions of the network input, a gradient of the objective function with respect to the neural network parameters of the neural network when the objective function is evaluated on a network output generated by the neural network by processing the augmented version of the network input;
determining a combined gradient for the network input by combining the gradients determined for the plurality of augmented versions of the network input; and
generating the clipped gradient for the network input by clipping the combined gradient for the network input; and
updating the neural network parameters using the clipped gradients for the network inputs of the plurality of network inputs.
2 . The method of claim 1 , wherein generating the plurality of augmented versions of the network input comprises:
obtaining a plurality of augmentation transformations, comprising, for each augmentation transformation, randomly sampling parameters defining the augmentation transformation; and
generating each augmented version of the network input by applying a respective augmentation transformation to the network input.
3 . The method of claim 1 , wherein determining a gradient of the objective function for an augmented version of the network input comprises:
processing the augmented version of the network input using the neural network, in accordance with current values of the neural network parameters of the neural network, to generate a corresponding network output; and
determining gradients of the objective function with respect to the neural network parameters of the neural network when the objective function is evaluated on the network output.
4 . The method of claim 1 , wherein determining the combined gradient for the network input comprises:
averaging the gradients determined for the plurality of augmented versions of the network input.
5 . The method of claim 1 , wherein for one or more of the network inputs, generating the clipped gradient for the network input comprises:
scaling the combined gradient for the network input to cause a norm of the combined gradient for the network input to satisfy a clipping threshold.
6 . The method of claim 5 , wherein scaling the combined gradient for the network input to cause the norm of the combined gradient for the network input to satisfy the clipping threshold comprises:
scaling the combined gradient for the network input by a scaling factor defined as a ratio of: (i) the clipping threshold, and (ii) the norm of the combined gradient for the network input.
7 . The method of claim 1 , further comprising, before updating the neural network parameters using the clipped gradients for the network inputs of the plurality of network inputs:
generating a set of noise parameters, comprising randomly sampling the noise parameters from a noise distribution; and
applying the noise parameters to the clipped gradients for the network inputs of the plurality of network inputs.
8 . The method of claim 7 , wherein the noise distribution comprises a Gaussian noise distribution.
9 . The method of claim 1 , wherein the neural network does not include any batch normalization layers.
10 . The method of claim 1 , wherein the neural network includes group normalization layers.
11 . The method of claim 1 , wherein the neural network is configured to process a network input comprising an image.
12 . The method of claim 1 , wherein the neural network is configured to process a network input comprising audio data.
13 . The method of claim 1 , wherein the neural network is configured to process a network input comprising electronic medical record data.
14 . The method of claim 1 , wherein the neural network is configured to process a network input comprising textual data.
15 . The method of claim 1 , wherein the neural network comprises one or more convolutional neural network layers.
16 . The method of claim 1 , wherein the objective function comprises a classification loss.
17 . The method of claim 1 , wherein at each training iteration, the plurality of network inputs comprises at least 4000 network inputs.
18 . The method of claim 1 , wherein generating a plurality of augmented versions of the network input comprises generating at least 8 augmented versions of the network input.
19 . One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:
training a set of neural network parameters of the neural network on a set of training data over a plurality of training iterations to optimize an objective function, comprising, at each training iteration:
sampling a plurality of network inputs from the set of training data;
determining a clipped gradient for each network input of the plurality of network inputs, comprising, for each network input of the plurality of network inputs:
generating a plurality of augmented versions of the network input, wherein each augmented version of the network input results from applying a respective augmentation transformation to the network input;
determining, for each of the plurality of augmented versions of the network input, a gradient of the objective function with respect to the neural network parameters of the neural network when the objective function is evaluated on a network output generated by the neural network by processing the augmented version of the network input;
determining a combined gradient for the network input by combining the gradients determined for the plurality of augmented versions of the network input; and
generating the clipped gradient for the network input by clipping the combined gradient for the network input; and
updating the neural network parameters using the clipped gradients for the network inputs of the plurality of network inputs.
20 . A system comprising one or more computers and one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
training a set of neural network parameters of the neural network on a set of training data over a plurality of training iterations to optimize an objective function, comprising, at each training iteration:
sampling a plurality of network inputs from the set of training data;
determining a clipped gradient for each network input of the plurality of network inputs, comprising, for each network input of the plurality of network inputs:
generating a plurality of augmented versions of the network input, wherein each augmented version of the network input results from applying a respective augmentation transformation to the network input;
determining, for each of the plurality of augmented versions of the network input, a gradient of the objective function with respect to the neural network parameters of the neural network when the objective function is evaluated on a network output generated by the neural network by processing the augmented version of the network input;
determining a combined gradient for the network input by combining the gradients determined for the plurality of augmented versions of the network input; and
generating the clipped gradient for the network input by clipping the combined gradient for the network input; and
updating the neural network parameters using the clipped gradients for the network inputs of the plurality of network inputs.