  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of outdated, private, and irrelevant data.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outdated, private, or irrelevant data, and its support for continual model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.
