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 Challenges related to privacy and model efficiency arise from the use of outdated, private, and irrelevant data.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 FRAMU's strength lies in its adaptability to fluctuating data landscapes, its ability to unlearn outdated, private, or irrelevant data, and its support for continual model evolution without compromising privacy.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：摘要：机器遗忘是一个新兴领域，通过允许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、私有及无关的数据会引发与隐私和模型效率相关的挑战，这些问题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，我们提出了一种新型框架——基于注意力机制与联邦强化学习的机器遗忘（FRAMU）。该框架整合了自适应学习机制、隐私保护技术和优化策略，为处理单模态或多模态等多种数据源提供了全面的解决方案，同时保持准确性与隐私性。FRAMU的优势在于其对动态数据环境的适应能力、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演化的特性。我们在单模态与多模态数据集上开展的实验表明，FRAMU显著优于基准模型。对收敛行为与优化策略的进一步评估也验证了该框架在联邦学习应用中的实用性。总体而言，FRAMU通过提供一种鲁棒且保护隐私的解决方案推动了机器遗忘的发展，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。