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GRIN+: Towards Fast Yet Effective Machine Unlearning for Imbalanced Medical Data — arXiv:2609.15571

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.15571
License (as archived): CC0-1.0
Archived: 2026-10-15T22:48:01.292Z
SHA-256 of the archived text: e402a47e1dd0c51bbbc53d7ed1f159e845b3de4eff7b64756f8ed2a447ee36c6
Stable URL of this page: https://metraipolis.com/documento/dc_ef048fcd4566

How to cite

GRIN+: Towards Fast Yet Effective Machine Unlearning for Imbalanced Medical Data — arXiv:2609.15571 — arXiv (en), https://arxiv.org/abs/2609.15571 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ef048fcd4566 (Metraipolis archive dc_ef048fcd4566, sha256 e402a47e1dd0c51bbbc53d7ed1f159e845b3de4eff7b64756f8ed2a447ee36c6)

BibTeX

@misc{dcef048fcd4566,
  title = {GRIN+: Towards Fast Yet Effective Machine Unlearning for Imbalanced Medical Data — arXiv:2609.15571},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.15571},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ef048fcd4566; sha256 e402a47e1dd0c51bbbc53d7ed1f159e845b3de4eff7b64756f8ed2a447ee36c6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.15571. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

As deep learning models become fundamental to modern healthcare, the "Right to be Forgotten" mandated by privacy regulations like GDPR and HIPAA necessitates effective machine unlearning (MU) to remove sensitive patient data from trained models. However, existing MU techniques often struggle with a fundamental "privacy-efficiency-utility" (PEU) trilemma, particularly in medical scenarios where data is frequently characterized by severe class imbalance and long-tailed distributions. In such cases, standard unlearning methods can fail to protect key clinical knowledge or mistakenly delete features essential for diagnosing rare conditions due to the gradient dominance of majority classes. To address these challenges, we propose GRIN+, a novel machine unlearning framework designed for fast and precise data erasure in imbalanced medical scenarios. GRIN+ decouples unlearning-specific knowledge from generalized representations at the parameter level by analyzing the gradient contributions of both "forget" and "retain" sets. It introduces a class-adaptive influence scoring mechanism to rectify gradient dominance and employs a direction-constrained update strategy to prevent the unintended erosion of vital clinical knowledge. Comprehensive benchmarking across multiple medical datasets, including skin cancer (ISIC), brain tumor (MRI), and breast ultrasound (BUSI), demonstrates that GRIN+ achieves an optimal balance of the PEU trilemma. Experimental results show that GRIN+ maintains high diagnostic accuracy and robust privacy while significantly enhancing runtime efficiency compared to existing baselines. We open-source the GRIN+ code and benchmarks to support further research.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.15571 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T22:29:03.331Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).