FRAMU : Attention-based Machine Unlearning using Federated Reinforcement Learning

Thanveer SHAIK, Xiaohui TAO, Lin LI, Haoran XIE, Taotao CAI, Xiaofeng ZHU, Qing LI

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Machine Unlearning, a pivotal field addressing data privacy in machine learning, necessitates efficient methods for the removal of private or irrelevant data. In this context, significant challenges arise, particularly in maintaining privacy and ensuring model efficiency when managing outdated, private, and irrelevant data. Such data not only compromises model accuracy but also burdens computational efficiency in both learning and unlearning processes. 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 FRAMU's strengths include its adaptability in fluctuating data landscapes, its ability to unlearn outdated, private, or irrelevant data, and its support for continual model evolution without compromising privacy.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.
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Early online date2 Apr 2024
DOIs
Publication statusE-pub ahead of print - 2 Apr 2024

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Adaptation models
  • Attention Mechanism
  • Data models
  • Data privacy
  • Distributed databases
  • Federated Learning
  • Federated learning
  • Machine Unlearning
  • Privacy
  • Reinforcement Learning
  • Reinforcement learning

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