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Privacy-preserving Learning via Deep Net Pruning
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This paper attempts to answer the question whether neural network pruning can be used as a tool to achieve differential privacy without losing much data utility. As a first step towards understanding the relationship between neural network pruning and differential privacy, this paper proves that pruning a given layer of the neural network is equivalent to adding a certain amount of differentially private noise to its hidden-layer activations. The paper also presents experimental results to show the practical implications of the theoretical finding and the key parameter values in a simple practical setting. These results show that neural network pruning can be a more effective alternative to adding differentially private noise for neural networks.
Forward citations
Cited by 2 Pith papers
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AdaDPIGU: Differentially Private SGD with Adaptive Clipping and Importance-Based Gradient Updates for Deep Neural Networks
A DP-SGD variant using top-60% gradient sparsification and coordinate-wise adaptive clipping is proposed; its privacy guarantee is not established for the actual algorithm because the mask comes from private data.
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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization
A PhD dissertation showing unified compression theory, personalized accelerated local training, and pruning methods that reduce communication costs in federated learning and maintain accuracy in LLM pruning.
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