GENIU: A Restricted Data Access Unlearning for Imbalanced Data

• URL: http://arxiv.org/abs/2406.07885v1
• Date: Wed, 12 Jun 2024 05:24:53 GMT
• Title: GENIU: A Restricted Data Access Unlearning for Imbalanced Data
• Authors: Chenhao Zhang, Shaofei Shen, Yawen Zhao, Weitong Tony Chen, Miao Xu,
• Abstract summary: Class unlearning involves enabling a trained model to forget data belonging to a specific class learned before. GENIU is the first practical framework for class unlearning imbalanced data settings and restricted data access.
• Score: 7.854651232997996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increasing emphasis on data privacy, the significance of machine unlearning has grown substantially. Class unlearning, which involves enabling a trained model to forget data belonging to a specific class learned before, is important as classification tasks account for the majority of today's machine learning as a service (MLaaS). Retraining the model on the original data, excluding the data to be forgotten (a.k.a forgetting data), is a common approach to class unlearning. However, the availability of original data during the unlearning phase is not always guaranteed, leading to the exploration of class unlearning with restricted data access. While current unlearning methods with restricted data access usually generate proxy sample via the trained neural network classifier, they typically focus on training and forgetting balanced data. However, the imbalanced original data can cause trouble for these proxies and unlearning, particularly when the forgetting data consists predominantly of the majority class. To address this issue, we propose the GENerative Imbalanced Unlearning (GENIU) framework. GENIU utilizes a Variational Autoencoder (VAE) to concurrently train a proxy generator alongside the original model. These generated proxies accurately represent each class and are leveraged in the unlearning phase, eliminating the reliance on the original training data. To further mitigate the performance degradation resulting from forgetting the majority class, we introduce an in-batch tuning strategy that works with the generated proxies. GENIU is the first practical framework for class unlearning in imbalanced data settings and restricted data access, ensuring the preservation of essential information for future unlearning. Experimental results confirm the superiority of GENIU over existing methods, establishing its effectiveness in empirical scenarios.

Related papers

• Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning [28.35038726318893]
  Machine learning models trained on vast amounts of real or synthetic data often achieve outstanding predictive performance across various domains. To address privacy concerns, machine unlearning has been proposed to erase specific data samples from models. We introduce the Unlearning Usability Attack to distill data distribution information into a small set of benign data.
  arXiv  Detail & Related papers  (2024-07-06T15:42:28Z)
• Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation [46.86767774669831]
  We propose a more effective and efficient federated unlearning scheme based on the concept of model explanation. We select the most influential channels within an already-trained model for the data that need to be unlearned.
  arXiv  Detail & Related papers  (2024-06-18T11:43:20Z)
• Blockchain-enabled Trustworthy Federated Unlearning [50.01101423318312]
  Federated unlearning is a promising paradigm for protecting the data ownership of distributed clients. Existing works require central servers to retain the historical model parameters from distributed clients. This paper proposes a new blockchain-enabled trustworthy federated unlearning framework.
  arXiv  Detail & Related papers  (2024-01-29T07:04:48Z)
• Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory [36.4406505365313]
  This paper presents a framework for federated class incremental learning that utilizes a generative model to synthesize samples from past distributions instead of storing part of past data. The generative model is trained on the server using data-free methods at the end of each task without requesting data from clients.
  arXiv  Detail & Related papers  (2023-07-02T07:06:45Z)
• A Survey on Class Imbalance in Federated Learning [6.632451878730774]
  Federated learning allows multiple client devices in a network to jointly train a machine learning model without direct exposure of clients' data. It has been found that models trained with federated learning usually have worse performance than their counterparts trained in the standard centralized learning mode.
  arXiv  Detail & Related papers  (2023-03-21T08:34:23Z)
• Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers [71.70205894168039]
  We consider instance-wise unlearning, of which the goal is to delete information on a set of instances from a pre-trained model. We propose two methods that reduce forgetting on the remaining data: 1) utilizing adversarial examples to overcome forgetting at the representation-level and 2) leveraging weight importance metrics to pinpoint network parameters guilty of propagating unwanted information.
  arXiv  Detail & Related papers  (2023-01-27T07:53:50Z)
• Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks [65.34113135080105]
  We show that not only the issue of data heterogeneity in current setups is not necessarily a problem but also in fact it can be beneficial for the FL participants. Our observations are intuitive. Our code is available at https://github.com/MMorafah/FL-SC-NIID.
  arXiv  Detail & Related papers  (2022-09-30T17:15:19Z)
• CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
  Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
  arXiv  Detail & Related papers  (2022-06-01T03:02:07Z)
• Machine Unlearning of Features and Labels [72.81914952849334]
  We propose first scenarios for unlearning and labels in machine learning models. Our approach builds on the concept of influence functions and realizes unlearning through closed-form updates of model parameters.
  arXiv  Detail & Related papers  (2021-08-26T04:42:24Z)
• Decentralized Federated Learning Preserves Model and Data Privacy [77.454688257702]
  We propose a fully decentralized approach, which allows to share knowledge between trained models. Students are trained on the output of their teachers via synthetically generated input data. The results show that an untrained student model, trained on the teachers output reaches comparable F1-scores as the teacher.
  arXiv  Detail & Related papers  (2021-02-01T14:38:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.