Learning to Unlearn while Retaining: Combating Gradient Conflicts in Machine Unlearning

• URL: http://arxiv.org/abs/2503.06339v1
• Date: Sat, 08 Mar 2025 21:00:36 GMT
• Title: Learning to Unlearn while Retaining: Combating Gradient Conflicts in Machine Unlearning
• Authors: Gaurav Patel, Qiang Qiu,
• Abstract summary: We propose Learning to Unlearn while Retaining, aimed to mitigate gradient conflicts between unlearning and retention objectives.<n>Our approach strategically avoids conflicts through an implicit gradient regularization mechanism that emerges naturally within the proposed framework.<n>We validate our approach across both discriminative and generative tasks, demonstrating its effectiveness in achieving unlearning without compromising performance on remaining data.
• Score: 20.479485237420985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine Unlearning has recently garnered significant attention, aiming to selectively remove knowledge associated with specific data while preserving the model's performance on the remaining data. A fundamental challenge in this process is balancing effective unlearning with knowledge retention, as naive optimization of these competing objectives can lead to conflicting gradients, hindering convergence and degrading overall performance. To address this issue, we propose Learning to Unlearn while Retaining, aimed to mitigate gradient conflicts between unlearning and retention objectives. Our approach strategically avoids conflicts through an implicit gradient regularization mechanism that emerges naturally within the proposed framework. This prevents conflicting gradients between unlearning and retention, leading to effective unlearning while preserving the model's utility. We validate our approach across both discriminative and generative tasks, demonstrating its effectiveness in achieving unlearning without compromising performance on remaining data. Our results highlight the advantages of avoiding such gradient conflicts, outperforming existing methods that fail to account for these interactions.

Related papers

• FALCON: Fine-grained Activation Manipulation by Contrastive Orthogonal Unalignment for Large Language Model [23.69222300760814]
  We propose Fine-grained Activation manipuLation by Contrastive Orthogonal uNalignment (FALCON) as a representation-guided unlearning approach.<n>FALCON achieves superior unlearning effectiveness while maintaining model utility, exhibiting robust resistance against knowledge recovery attempts.
  arXiv  Detail & Related papers  (2025-02-03T16:05:15Z)
• Memory-efficient Continual Learning with Neural Collapse Contrastive [5.843533603338313]
  Contrastive learning has significantly improved representation quality, enhancing knowledge transfer across tasks in continual learning (CL)<n>However, catastrophic forgetting remains a key challenge, as contrastive based methods primarily focus on "soft relationships" or "softness" between samples.<n>We propose Focal Neural Collapse Contrastive (FNC2), a novel representation learning loss that effectively balances both soft and hard relationships.
  arXiv  Detail & Related papers  (2024-12-03T22:00:12Z)
• Temporal-Difference Variational Continual Learning [89.32940051152782]
  A crucial capability of Machine Learning models in real-world applications is the ability to continuously learn new tasks. In Continual Learning settings, models often struggle to balance learning new tasks with retaining previous knowledge. We propose new learning objectives that integrate the regularization effects of multiple previous posterior estimations.
  arXiv  Detail & Related papers  (2024-10-10T10:58:41Z)
• Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios [18.73206066109299]
  Mini-Unlearning is a novel approach that capitalizes on a critical observation: unlearned parameters correlate with retrained parameters through contraction mapping. This lightweight, scalable method significantly enhances model accuracy and strengthens resistance to membership inference attacks. Our experiments demonstrate that Mini-Unlearning not only works under higher unlearning ratios but also outperforms existing techniques in both accuracy and security.
  arXiv  Detail & Related papers  (2024-06-24T01:43:30Z)
• Towards Effective Evaluations and Comparisons for LLM Unlearning Methods [97.2995389188179]
  This paper seeks to refine the evaluation of machine unlearning for large language models.<n>It addresses two key challenges -- the robustness of evaluation metrics and the trade-offs between competing goals.
  arXiv  Detail & Related papers  (2024-06-13T14:41:00Z)
• Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning [2.3558144417896583]
  The planning problem constitutes a fundamental aspect of the autonomous driving framework. We propose Validity Learning on Failures, VL(on failure) as a remedy to address this issue. We show that VL(on failure) outperforms the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2024-06-03T17:25:18Z)
• Relaxed Contrastive Learning for Federated Learning [48.96253206661268]
  We propose a novel contrastive learning framework to address the challenges of data heterogeneity in federated learning. Our framework outperforms all existing federated learning approaches by huge margins on the standard benchmarks.
  arXiv  Detail & Related papers  (2024-01-10T04:55:24Z)
• Exploring Federated Unlearning: Analysis, Comparison, and Insights [101.64910079905566]
  federated unlearning enables the selective removal of data from models trained in federated systems. This paper examines existing federated unlearning approaches, examining their algorithmic efficiency, impact on model accuracy, and effectiveness in preserving privacy. We propose the OpenFederatedUnlearning framework, a unified benchmark for evaluating federated unlearning methods.
  arXiv  Detail & Related papers  (2023-10-30T01:34:33Z)
• Resilient Constrained Learning [94.27081585149836]
  This paper presents a constrained learning approach that adapts the requirements while simultaneously solving the learning task. We call this approach resilient constrained learning after the term used to describe ecological systems that adapt to disruptions by modifying their operation.
  arXiv  Detail & Related papers  (2023-06-04T18:14:18Z)
• Solving Inefficiency of Self-supervised Representation Learning [87.30876679780532]
  Existing contrastive learning methods suffer from very low learning efficiency. Under-clustering and over-clustering problems are major obstacles to learning efficiency. We propose a novel self-supervised learning framework using a median triplet loss.
  arXiv  Detail & Related papers  (2021-04-18T07:47:10Z)

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.