<ul><li>Differential privacy (DP) in deep learning is a critical concern for maintaining data confidentiality and model utility.</li><li>Forward-learning algorithms add noise during the forward pass to estimate gradients, providing potential natural differential privacy protection.</li><li>A new algorithm, DP-ULR, is introduced as a privatized forward-learning algorithm with differential privacy guarantees.</li><li>DP-ULR achieves competitive performance compared to traditional differential privacy training algorithms based on backpropagation.</li></ul>

Forward Learning with Differential Privacy

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