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IDSISA: Influence-Distilled Sharded Training for Efficient Machine Unlearning

IDSISA improves SISA-based machine unlearning by using influence functions to concentrate forget-prone samples into fewer shards, reducing retraining overhead, and applying distillation from unaffected shards to recover utility lost to isolation. It preserves SISA's exact deletion guarantee and adds a bounded approximation for utility recovery, with formal handling of repeated deletion sequences via cascading retraining. Experiments across multiple architectures show 14-91% better selective forgetting and up to 50% relative utility retention gains over SISA.

Why it matters: This paper presents a concrete algorithmic improvement with formal guarantees and empirical results, directly relevant to privacy-compliant AI systems.