Backdoor Attack:在某些想要 unlearning 的类数据上添加 trigger,测试模型能否触发 trigger; Data Poisoning Attack:训练过程中进行投毒攻击, Membership Inference Attack. 参考 ^Carlini, Nicholas, et al. "Extracting training data from large language models." 30th USENIX Security Symposium (USENIX Security 21)...
First, overfitting has long been a fundamental issue in machine learning, occurring when a model memorizes the training data but struggles to generalize to new, unseen test data. To enhance generalization, it is advantageous for the model to avoidmemorizationand instead focus on learning the genuine...
Machine Unlearning removes specific knowledge about training data samples from an already trained model. It has significant practical benefits, such as purging private, inaccurate, or outdated information from trained models without the need for complete re-training. Unlearning within a multimodal setting...
Federated Learning: Collaborative Machine Learning without Centralized Training Data - Google AI Blog 2017 Go Federated with OpenFL - Intel 2021 Open-Sources Developing a federated learning framework from scratch is very time-consuming, especially in industrial. An excellent FL framework can facilitate en...
Towards Machine Unlearning Benchmarks: Forgetting the Personal Identities in Facial Recognition Systems This repository provides practical benchmark datasets and PyTorch implementations for Machine Unlearning, enabling the construction of privacy-crucial AI systems by forgetting specific data instances without ch...
The way AI systems work means that we can’t easily delete what they have learned. Now, researchers are seeking ways to remove sensitive information without having to retrain them from scratch
Base Model Training:Train multiple base models on the training data. Meta-Model Training:Use the predictions of the base models as features to train a meta-model. Example: A typical stacking ensemble might use logistic regression as the meta-model and decision trees, SVMs, and KNNs as base ...
10.2 Machine Unlearning 10.2.1 Problem Overview Machine unlearning [13], [227], a recent area of research, addresses the need to forget previously learned training data in order to protect user data privacy and aligns with privacy regulations such as the European Union's General Data Protection ...
Unlearning guarantees that training on a point and unlearning it afterwards will produce the same distribution of models that not training on the point at all. 下图解释了Unlearning的目的和难点。粗暴的做法就是挑出需要被遗忘的数据,然后Reinitialization重新训练,这无疑很慢,且浪费了已经训练的部分。 源自...
PapersUnlearning MethodsUnlearning TargetTraining DatasetIntermediatesUnlearned Samples’ TypeTarget Models’ TypeConsistencyAccuracyVerifiability Graves et al. (DBLP:conf/aaai/GravesNG21)Data ObfuscationStrong unlearningYesNoSamples or ClassDNNNoNoAttack-Based ...