Interference-Aware Multi-Task Unlearning

Machine unlearning typically focuses on single-task settings, but modern AI models often operate in multi-task environments with shared backbones, leading to unintended interference when data is removed. This paper introduces multi-task unlearning, proposing an interference-aware framework that uses task-aware gradient projection to address task-level and instance-level interference.