On the Necessity of Output Distribution Reweighting for Effective Class Unlearning

The paper titled 'Enhanced Structured Lasso Pruning with Class-wise Information' discusses advancements in neural network pruning methods. Traditional pruning techniques often overlook class-wise information, leading to potential loss of statistical data. This study introduces two new pruning schemes, sparse graph-structured lasso pruning with Information Bottleneck (sGLP-IB) and sparse tree-guided lasso pruning with Information Bottleneck (sTLP-IB), aimed at preserving statistical information while reducing model complexity.

The paper titled 'AMUN: Adversarial Machine UNlearning' discusses a novel method for machine unlearning, which allows users to delete specific datasets to comply with privacy regulations. Traditional exact unlearning methods require significant computational resources, while approximate methods have not achieved satisfactory accuracy. The proposed Adversarial Machine UNlearning (AMUN) technique enhances model performance by fine-tuning on adversarial examples, effectively reducing model confidence on forgotten samples while maintaining accuracy on test datasets.

The article discusses FedOrtho, a federated unlearning framework designed to enhance data unlearning in federated learning environments. It addresses the challenges of balancing forgetting and retention, particularly in non-IID settings. FedOrtho employs orthogonalized deep convolutional kernels and a one-shot soft pruning mechanism, achieving state-of-the-art performance on datasets like CIFAR-10 and TinyImageNet, with over 98% forgetting quality and 97% retention accuracy.

The paper introduces PrivDFS, a distributed feature-sharing framework designed for input-private inference in image classification. It addresses vulnerabilities in split inference that allow Data Reconstruction Attacks (DRAs) to reconstruct inputs with high fidelity. By fragmenting the intermediate representation and processing these fragments independently across a majority-honest set of servers, PrivDFS limits the reconstruction capability while maintaining accuracy within 1% of non-private methods.