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@@ -47,7 +47,7 @@ def generate_noise(max_norm, parameter, noise_multiplier, noise_type, device):
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return 0.0
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# Server side Noise
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-def apply_noise(weights, batch_size, noise_multiplier, noise_type, device, loss_reduction="mean"):
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+def apply_noise(weights, batch_size, max_norm, noise_multiplier, noise_type, device, loss_reduction="mean"):
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"""
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A function for applying noise to weights on the (intermediate) server side that utilizes the generate_noise function above.
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@@ -55,6 +55,9 @@ def apply_noise(weights, batch_size, noise_multiplier, noise_type, device, loss_
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The weights to which to apply the noise.
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@param batch_size
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Batch size used for averaging.
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+ @param max_norm
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+ The maximum norm of the per-sample gradients. Any gradient with norm
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+ higher than this will be clipped to this value.
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@param noise_multiplier
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The ratio of the standard deviation of the Gaussian noise to
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the L2-sensitivity of the function to which the noise is added
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@@ -68,7 +71,7 @@ def apply_noise(weights, batch_size, noise_multiplier, noise_type, device, loss_
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currently supported: mean
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"""
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for p in weights.values():
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- noise = generate_noise(0, p, noise_multiplier, noise_type, device)
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+ noise = generate_noise(max_norm, p, noise_multiplier, noise_type, device)
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if loss_reduction == "mean":
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noise /= batch_size
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p += noise
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