Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening

• URL: http://arxiv.org/abs/2308.07707v2
• Date: Wed, 13 Dec 2023 16:11:58 GMT
• Title: Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening
• Authors: Jack Foster, Stefan Schoepf, Alexandra Brintrup
• Abstract summary: Selective Synaptic Dampening (SSD) is a fast, performant, and does not require long-term storage of the training data. We present a novel two-step, post hoc, retrain-free approach to machine unlearning which is fast, performant, and does not require long-term storage of the training data.
• Score: 51.34904967046097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine unlearning, the ability for a machine learning model to forget, is becoming increasingly important to comply with data privacy regulations, as well as to remove harmful, manipulated, or outdated information. The key challenge lies in forgetting specific information while protecting model performance on the remaining data. While current state-of-the-art methods perform well, they typically require some level of retraining over the retained data, in order to protect or restore model performance. This adds computational overhead and mandates that the training data remain available and accessible, which may not be feasible. In contrast, other methods employ a retrain-free paradigm, however, these approaches are prohibitively computationally expensive and do not perform on par with their retrain-based counterparts. We present Selective Synaptic Dampening (SSD), a novel two-step, post hoc, retrain-free approach to machine unlearning which is fast, performant, and does not require long-term storage of the training data. First, SSD uses the Fisher information matrix of the training and forgetting data to select parameters that are disproportionately important to the forget set. Second, SSD induces forgetting by dampening these parameters proportional to their relative importance to the forget set with respect to the wider training data. We evaluate our method against several existing unlearning methods in a range of experiments using ResNet18 and Vision Transformer. Results show that the performance of SSD is competitive with retrain-based post hoc methods, demonstrating the viability of retrain-free post hoc unlearning approaches.

Related papers

• Edge Unlearning is Not "on Edge"! An Adaptive Exact Unlearning System on Resource-Constrained Devices [26.939025828011196]
  The right to be forgotten mandates that machine learning models enable the erasure of a data owner's data and information from a trained model. We propose a Constraint-aware Adaptive Exact Unlearning System at the network Edge (CAUSE) to enable exact unlearning on resource-constrained devices.
  arXiv  Detail & Related papers  (2024-10-14T03:28:09Z)
• Towards Robust and Cost-Efficient Knowledge Unlearning for Large Language Models [25.91643745340183]
  Large Language Models (LLMs) have demonstrated strong reasoning and memorization capabilities via pretraining on massive textual corpora. This poses risk of privacy and copyright violations, highlighting the need for efficient machine unlearning methods. We propose two novel techniques for robust and efficient unlearning for LLMs.
  arXiv  Detail & Related papers  (2024-08-13T04:18:32Z)
• Partially Blinded Unlearning: Class Unlearning for Deep Networks a Bayesian Perspective [4.31734012105466]
  Machine Unlearning is the process of selectively discarding information designated to specific sets or classes of data from a pre-trained model. We propose a methodology tailored for the purposeful elimination of information linked to a specific class of data from a pre-trained classification network. Our novel approach, termed textbfPartially-Blinded Unlearning (PBU), surpasses existing state-of-the-art class unlearning methods, demonstrating superior effectiveness.
  arXiv  Detail & Related papers  (2024-03-24T17:33:22Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• Dataset Condensation Driven Machine Unlearning [0.0]
  Current trend in data regulation requirements and privacy-preserving machine learning has emphasized the importance of machine unlearning. We propose new dataset condensation techniques and an innovative unlearning scheme that strikes a balance between machine unlearning privacy, utility, and efficiency. We present a novel and effective approach to instrumenting machine unlearning and propose its application in defending against membership inference and model inversion attacks.
  arXiv  Detail & Related papers  (2024-01-31T21:48:25Z)
• Robust Machine Learning by Transforming and Augmenting Imperfect Training Data [6.928276018602774]
  This thesis explores several data sensitivities of modern machine learning. We first discuss how to prevent ML from codifying prior human discrimination measured in the training data. We then discuss the problem of learning from data containing spurious features, which provide predictive fidelity during training but are unreliable upon deployment.
  arXiv  Detail & Related papers  (2023-12-19T20:49:28Z)
• Unlearn What You Want to Forget: Efficient Unlearning for LLMs [92.51670143929056]
  Large language models (LLMs) have achieved significant progress from pre-training on and memorizing a wide range of textual data. This process might suffer from privacy issues and violations of data protection regulations. We propose an efficient unlearning framework that could efficiently update LLMs without having to retrain the whole model after data removals.
  arXiv  Detail & Related papers  (2023-10-31T03:35:59Z)
• SAFE: Machine Unlearning With Shard Graphs [100.12621304361288]
  We present Synergy Aware Forgetting Ensemble (SAFE), a method to adapt large models on a diverse collection of data. SAFE uses a lightweight system of adapters which can be trained while reusing most of the computations. This allows SAFE to be trained on shards an order-of-magnitude smaller than current state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-04-25T22:02:09Z)
• Knowledge Distillation as Efficient Pre-training: Faster Convergence, Higher Data-efficiency, and Better Transferability [53.27240222619834]
  Knowledge Distillation as Efficient Pre-training aims to efficiently transfer the learned feature representation from pre-trained models to new student models for future downstream tasks. Our method performs comparably with supervised pre-training counterparts in 3 downstream tasks and 9 downstream datasets requiring 10x less data and 5x less pre-training time.
  arXiv  Detail & Related papers  (2022-03-10T06:23:41Z)
• 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)

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.