Deeraj SK

For machine unlearning under privacy rules, IDSISA adds influence-guided shard assignment and cross-shard distillation to SISA's shard-retraining framework. Influence scores are computed once before training and co-locate samples likely to be deleted together, so a deletion request touches fewer shards than under random partitioning; retrained shards also learn from unaffected shards' soft outputs to recover utility lost to isolation. On ResNet-18/CIFAR-10, BiLSTM/IMDB, TCN/IMDB and a fully connected Fashion-MNIST network, the deletion guarantee still comes from retraining itself, and distillation is only a separately quantified utility-recovery step.

For machine unlearning under privacy rules, IDSISA adds influence-guided shard assignment and cross-shard distillation to SISA's shard-retraining framework. Influence scores are computed once before training and co-locate samples likely to be deleted together, so a deletion request touches fewer shards than under random partitioning; retrained shards also learn from unaffected shards' soft outputs to recover utility lost to isolation. On ResNet-18/CIFAR-10, BiLSTM/IMDB, TCN/IMDB and a fully connected Fashion-MNIST network, the deletion guarantee still comes from retraining itself, and distillation is only a separately quantified utility-recovery step.