FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning

TL;DR

FRAMU is a novel attention-based federated reinforcement learning framework that enables efficient, privacy-preserving machine unlearning, effectively removing outdated or private data while maintaining model accuracy and adaptability in dynamic data environments.

Contribution

It introduces a comprehensive framework combining attention mechanisms, federated reinforcement learning, and privacy techniques for effective machine unlearning in diverse data settings.

Findings

FRAMU significantly outperforms baseline models in unlearning tasks.

The framework maintains high model accuracy after data removal.

FRAMU demonstrates robust adaptability to changing data landscapes.

Abstract

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 Challenges related to privacy and model efficiency arise from the use of outdated, private, and irrelevant data.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 FRAMU's strength lies in its adaptability to fluctuating data landscapes, its ability to unlearn outdated, private, or irrelevant data, and its support for continual model evolution without compromising privacy.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.