Machine Unlearning is an emerging field that addresses data privacy issues by enabling the removal of private or irrelevant data from the Machine Learning process. Challenges related to privacy and model efficiency arise from the use of outdated, private, and irrelevant data. These issues compromise both the accuracy and the computational efficiency of models in both Machine Learning and Unlearning. To mitigate these challenges, we introduce a novel framework, Attention-based Machine Unlearning using Federated Reinforcement Learning (FRAMU). This framework incorporates adaptive learning mechanisms, privacy preservation techniques, and optimization strategies, making it a well-rounded solution for handling various data sources, either single-modality or multi-modality, while maintaining accuracy and privacy. FRAMU's strength lies in its adaptability to fluctuating data landscapes, its ability to unlearn outdated, private, or irrelevant data, and its support for continual model evolution without compromising privacy. Our experiments, conducted on both single-modality and multi-modality datasets, revealed that FRAMU significantly outperformed baseline models. Additional assessments of convergence behavior and optimization strategies further validate the framework's utility in federated learning applications. Overall, FRAMU advances Machine Unlearning by offering a robust, privacy-preserving solution that optimizes model performance while also addressing key challenges in dynamic data environments.

翻译：机器遗忘是一个新兴领域，旨在通过从机器学习过程中移除私有或无关数据来解决数据隐私问题。使用过时、私有或无关的数据会引发隐私和模型效率方面的挑战，这些挑战会损害机器学习与遗忘过程中模型的准确性和计算效率。为应对这些挑战，我们提出了一种新框架——基于注意力机制与联邦强化学习的机器遗忘框架（FRAMU）。该框架融合了自适应学习机制、隐私保护技术与优化策略，是一种能够处理单模态或多模态数据源的综合性解决方案，可在保持准确性和隐私性的前提下运作。FRAMU的优势在于其适应动态数据环境的能力、遗忘过时/私有/无关数据的能力，以及在不损害隐私的前提下支持模型持续进化。我们在单模态和多模态数据集上开展的实验表明，FRAMU显著优于基线模型。对收敛行为与优化策略的额外评估进一步验证了该框架在联邦学习应用中的实用性。总体而言，FRAMU通过提供一种鲁棒的隐私保护方案推动了机器遗忘领域的发展，该方案在优化模型性能的同时，也解决了动态数据环境中的关键挑战。