Probing then Editing: A Push-Pull Framework for Retain-Free Machine Unlearning in Industrial IoT

• URL: http://arxiv.org/abs/2511.09414v1
• Date: Thu, 13 Nov 2025 01:53:04 GMT
• Title: Probing then Editing: A Push-Pull Framework for Retain-Free Machine Unlearning in Industrial IoT
• Authors: Jiao Chen, Weihua Li, Jianhua Tang,
• Abstract summary: We propose a novel retain-free unlearning framework, referred to as Probing then Editing (PTE)<n>PTE frames unlearning as a probe-edit process and generates corresponding editing instructions using the model's own predictions.<n>Benefiting from this mechanism, PTE achieves efficient and balanced knowledge editing using only the to-be-forgotten data and the original model.
• Score: 6.973959179359068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In dynamic Industrial Internet of Things (IIoT) environments, models need the ability to selectively forget outdated or erroneous knowledge. However, existing methods typically rely on retain data to constrain model behavior, which increases computational and energy burdens and conflicts with industrial data silos and privacy compliance requirements. To address this, we propose a novel retain-free unlearning framework, referred to as Probing then Editing (PTE). PTE frames unlearning as a probe-edit process: first, it probes the decision boundary neighborhood of the model on the to-be-forgotten class via gradient ascent and generates corresponding editing instructions using the model's own predictions. Subsequently, a push-pull collaborative optimization is performed: the push branch actively dismantles the decision region of the target class using the editing instructions, while the pull branch applies masked knowledge distillation to anchor the model's knowledge on retained classes to their original states. Benefiting from this mechanism, PTE achieves efficient and balanced knowledge editing using only the to-be-forgotten data and the original model. Experimental results demonstrate that PTE achieves an excellent balance between unlearning effectiveness and model utility across multiple general and industrial benchmarks such as CWRU and SCUT-FD.

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