论文全面回顾了这些解决方案,并识别和讨论了它们的优缺点。 Xu J, Wu Z, Wang C, et al. Machine unlearning: Solutions and challenges[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2024. 2 创新点 全面分类与分析:论文提供了对现有机器遗忘解决方案的全面分类,包括精确遗忘和近似遗...
Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects , verification and evaluation metrics, challenges and solutions for unlearning under different applications, as well as attacks targeting machine unlearning. ... N Li,C Zhou,Y Gao,... 被引量: 0发表: 2024年 Emerging ...
Machine unlearning is a service offered to customers to withdraw their privacy from trained models, but its value is yet to be thoroughly evaluated. In addition, the free unlearning service is insufficient due to unaffordable computational cost and degra
Interestingly, machine unlearning demonstrates two distinct phenomena related to forgetting. On one hand, it involves forgetting specific training data memorized by the pre-trained model. The problem of machine unlearning can be categorized into two main types: exact unlearning and approximate unlearning....
Addressing forgetting faces numerous challenges that vary across different research fields. These challenges include: 解决遗忘面临着许多不同研究领域不同的挑战。这些挑战包括: Data Availability:Data availability is a significant challenge in various scenarios and greatly complicates the task of addressing forg...
Si et al.Knowledge Unlearning for LLMs: Tasks, Methods, and ChallengesarXiv Sinha et al.Distill to Delete: Unlearning in Graph Networks with Knowledge DistillationarXiv Sun et al.Generative Adversarial Networks UnlearningarXiv Tan et al.Unfolded Self-Reconstruction LSH: Towards Machine Unlearning in...
Boosting algorithms have transformed machine learning, offering robust solutions to complex challenges across diverse fields. Here are some key applications that demonstrate their versatility and impact: Image Recognition and Computer Vision Boosting algorithms significantly improve image recognition and computer...
MYWL21; DBLP:conf/www/Wang0XQ22; DBLP:journals/corr/abs-2002-02730)Replaces partial parameters with pre-calculated parametersReduces the cost caused by intermediate storage; the unlearning process can be completed at a faster speedOnly applicable to partial models; not easy to implement and ...
Figure 1. Key takeaways from a survey of perspectives on the challenges posed by recent trends in dataset use in machine learning Definitions We follow Schlangen9 in distinguishing between benchmarks, tasks, capabilities, and datasets. While his work focused on NLP, we broaden these definitions ...
the implementation of technical solutions to simultaneously address the demands for data protection and utilization is mandatory. Here we present an overview of current and next-generation methods for federated, secure and privacy-preserving artificial intelligence with a focus on medical imaging application...