[Paper Review] Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience

Thanh Trung Huynh, Trong Bang Nguyen+5 more|arXiv (Cornell University)|May 28, 2024

Brain Tumor Detection and Classification4 citations

TL;DR

Fast-FedUL is a training-free federated unlearning method that removes a target client’s influence from a global FL model by analytically reversing historical gradients, achieving provable skew resilience. It reduces unlearning time by 1000× compared to retraining while eliminating backdoor attacks with 0.01% success rate and maintaining 98% main task accuracy.

ABSTRACT

Federated learning (FL) has recently emerged as a compelling machine learning paradigm, prioritizing the protection of privacy for training data. The increasing demand to address issues such as ``the right to be forgotten'' and combat data poisoning attacks highlights the importance of techniques, known as extit{unlearning}, which facilitate the removal of specific training data from trained FL models. Despite numerous unlearning methods proposed for centralized learning, they often prove inapplicable to FL due to fundamental differences in the operation of the two learning paradigms. Consequently, unlearning in FL remains in its early stages, presenting several challenges. Many existing unlearning solutions in FL require a costly retraining process, which can be burdensome for clients. Moreover, these methods are primarily validated through experiments, lacking theoretical assurances. In this study, we introduce Fast-FedUL, a tailored unlearning method for FL, which eliminates the need for retraining entirely. Through meticulous analysis of the target client's influence on the global model in each round, we develop an algorithm to systematically remove the impact of the target client from the trained model. In addition to presenting empirical findings, we offer a theoretical analysis delineating the upper bound of our unlearned model and the exact retrained model (the one obtained through retraining using untargeted clients). Experimental results with backdoor attack scenarios indicate that Fast-FedUL effectively removes almost all traces of the target client, while retaining the knowledge of untargeted clients (obtaining a high accuracy of up to 98\% on the main task). Significantly, Fast-FedUL attains the lowest time complexity, providing a speed that is 1000 times faster than retraining. Our source code is publicly available at \url{https://github.com/thanhtrunghuynh93/fastFedUL}.

Motivation & Objective

• Address the lack of efficient, retraining-free federated unlearning methods that can remove client-specific data without retraining.
• Overcome the limitations of existing unlearning methods in FL, which rely on costly retraining or lack theoretical guarantees.
• Ensure model utility is preserved post-unlearning by mitigating the skew introduced by gradient reversal in non-IID data settings.
• Provide theoretical bounds on the discrepancy between the unlearned model and the retrained model to ensure reliability.
• Optimize storage and computation by selecting only essential historical updates for unlearning, reducing memory overhead.

Proposed method

• Proposes a gradient reversal mechanism that systematically removes the influence of a target client’s historical updates on the global model without retraining.
• Introduces a client sampling strategy that prioritizes updates based on their significance to minimize storage and computation costs.
• Develops a theoretical framework to bound the model skew introduced by gradient reversal, enabling accurate utility recovery.
• Uses angle-based parameter deviation analysis to quantify model similarity between unlearned and retrained models, ensuring robustness.
• Employs a skew-resilient estimation technique that accumulates model skew over rounds to restore model utility post-unlearning.
• Applies knowledge distillation principles implicitly through skew-aware gradient correction, avoiding explicit distillation steps.

Experimental results

Research questions

• RQ1 Can a federated unlearning method eliminate client influence without retraining, while preserving model utility on untainted clients?
• RQ2 How can model skew introduced by gradient reversal be theoretically bounded and mitigated in non-IID data settings?
• RQ3 What is the impact of client sampling strategy on unlearning efficiency and model accuracy?
• RQ4 How does the proposed method compare to retraining in terms of unlearning effectiveness and backdoor mitigation?
• RQ5 Can a training-free approach achieve both high unlearning accuracy and low computational cost in federated learning?

Key findings

• Fast-FedUL achieves a backdoor attack success rate of only 0.01% on the unlearned model, effectively eliminating malicious patterns.
• The method maintains a main task accuracy of up to 98% on untainted clients, demonstrating strong knowledge preservation.
• Fast-FedUL reduces unlearning time by 1000× compared to retraining, making it highly efficient for real-world deployment.
• Theoretical analysis confirms that the unlearned model remains within a provable bound of the retrained model, ensuring reliability.
• Ablation studies show that random sampling and lack of skew mitigation reduce performance by up to 17%, validating the necessity of the proposed components.
• Qualitative analysis confirms that Fast-FedUL’s model parameters are within 10° angle deviation of the retrained model, with a concentrated distribution, indicating high fidelity.