arXiv cs.AI· Deeraj SK, Sadhana Devarajan, Tanmoy Hazra, Anupam Shukla·· 11 小时前AI 评分29
IDSISA：用影响函数蒸馏分片训练，实现高效机器遗忘
IDSISA: Influence-Distilled Sharded Training for Efficient Machine Unlearning
AI 导读
IDSISA 通过影响函数将易遗忘样本集中到少数分片，并从不受影响的分片蒸馏以恢复隔离造成的效用损失，在保留 SISA 精确删除保证的同时，蒸馏带来的效用恢复被单独量化为期望 ε_cert ≤ 0.015。
正文 · 原文
Abstract:Machine unlearning removes the influence of specific training examples to comply with privacy regulations. Full retraining gives strong guarantees but is computationally prohibitive at scale; approximate methods are efficient but lack formal guarantees. SISA (Sharded, Isolated, Sliced, and Aggregated training) achieves certified deletion through data sharding and independent retraining, but random partitioning scatters forget requests across most shards, and isolated retraining degrades utility. We introduce IDSISA (Influence-Distilled SISA), which uses influence functions to concentrate forget-prone samples into few shards and distillation from unaffected shards to recover utility lost to isolation. The deletion guarantee inherited from SISA's retraining step remains exact and requires no probabilistic qualification; the distillation-based utility recovery is a separately quantified, empirically bounded approximation ($\epsilon_{\text{cert}} \leq 0.015$ in expectation). We further formalize certification under sequences of repeated deletion requests, where cross-shard distillation can create dependencies between shards; we prove that a closure-based cascading retraining procedure restores the deletion guarantee to the full aggregated model under any deletion sequence. Experiments on ResNet-18/CIFAR-10, ResNet-18/CIFAR-100, BiLSTM/IMDB, TCN/IMDB, and a fully connected network on Fashion-MNIST show $14-91\%$ better selective forgetting, up to $50\%$ better utility retention in relative terms (with consistent percentage-point gains on the other architectures, where a relative baseline is undefined), and unlearning-time changes ranging from $-6.7\%$ to $+6.4\%$ depending on architecture, relative to SISA.
| Comments: | This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.06519 [cs.LG] |
| (or arXiv:2611.06519v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06519 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Deeraj S K [view email]
[v1]
Wed, 4 Nov 2026 13:45:32 UTC (956 KB)
来源：arXiv cs.AI · arxiv.org