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Exploring Federated Unlearning: Review, Comparison, and Insights

Yang Zhao, Jiaxi Yang, Yiling Tao, Lixu Wang, Xiaoxiao Li, Dusit Niyato, H. Vincent Poor

TL;DR

Federated unlearning addresses regulatory and privacy concerns by enabling selective removal of data influence in federated models. The paper categorizes unlearning methods into training-based, tuning-based, and model-based approaches, analyzes their privacy–accuracy–efficiency trade-offs, and introduces the OpenFederatedUnlearning benchmark to standardize evaluation. Through experiments on MNIST, CIFAR-10, and SVHN, it demonstrates that no single method is universally optimal and emphasizes scenario-driven selection, proposing practical benchmarks. The work highlights future directions, including energy-efficient unlearning at large scale and extending techniques to large language models, to make privacy-preserving federated learning more robust and widely adoptable.

Abstract

The increasing demand for privacy-preserving machine learning has spurred interest in federated unlearning, which enables the selective removal of data from models trained in federated systems. However, developing federated unlearning methods presents challenges, particularly in balancing three often conflicting objectives: privacy, accuracy, and efficiency. This paper provides a comprehensive analysis of existing federated unlearning approaches, examining their algorithmic efficiency, impact on model accuracy, and effectiveness in preserving privacy. We discuss key trade-offs among these dimensions and highlight their implications for practical applications across various domains. Additionally, we propose the OpenFederatedUnlearning framework, a unified benchmark for evaluating federated unlearning methods, incorporating classic baselines and diverse performance metrics. Our findings aim to guide practitioners in navigating the complex interplay of these objectives, offering insights to achieve effective and efficient federated unlearning. Finally, we outline directions for future research to further advance the state of federated unlearning techniques.

Exploring Federated Unlearning: Review, Comparison, and Insights

TL;DR

Federated unlearning addresses regulatory and privacy concerns by enabling selective removal of data influence in federated models. The paper categorizes unlearning methods into training-based, tuning-based, and model-based approaches, analyzes their privacy–accuracy–efficiency trade-offs, and introduces the OpenFederatedUnlearning benchmark to standardize evaluation. Through experiments on MNIST, CIFAR-10, and SVHN, it demonstrates that no single method is universally optimal and emphasizes scenario-driven selection, proposing practical benchmarks. The work highlights future directions, including energy-efficient unlearning at large scale and extending techniques to large language models, to make privacy-preserving federated learning more robust and widely adoptable.

Abstract

The increasing demand for privacy-preserving machine learning has spurred interest in federated unlearning, which enables the selective removal of data from models trained in federated systems. However, developing federated unlearning methods presents challenges, particularly in balancing three often conflicting objectives: privacy, accuracy, and efficiency. This paper provides a comprehensive analysis of existing federated unlearning approaches, examining their algorithmic efficiency, impact on model accuracy, and effectiveness in preserving privacy. We discuss key trade-offs among these dimensions and highlight their implications for practical applications across various domains. Additionally, we propose the OpenFederatedUnlearning framework, a unified benchmark for evaluating federated unlearning methods, incorporating classic baselines and diverse performance metrics. Our findings aim to guide practitioners in navigating the complex interplay of these objectives, offering insights to achieve effective and efficient federated unlearning. Finally, we outline directions for future research to further advance the state of federated unlearning techniques.
Paper Structure (14 sections, 2 figures, 3 tables)

This paper contains 14 sections, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Federated Unlearning Approaches, Applications, and Challenges
  3. Approaches
  4. Applications
  5. Challenges
  6. Evaluation Metrics
  7. Alignment-based Metrics
  8. Marking-based Metrics
  9. Metrics for Efficiency
  10. Experiments
  11. Experimental Setup
  12. Results Discussion
  13. Future Directions
  14. Conclusion

Figures (2)

  • Figure 1: Trade-offs between unlearning efficiency and model accuracy. As a machine learning model's complexity increases, its accuracy increases while its unlearning efficiency decreases.
  • Figure 2: Federated unlearning workflows in class-level unlearning, sample-level unlearning, and client-level unlearning scenarios. Each flow includes removing data from clients' datasets, locally updating models, aggregating updated models to revise the global model, and validating model accuracy before redeployment.