Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation

Zahir Alsulaimawi

TL;DR

This paper proposes a simplified consensus-based verification process integrated with an adaptive thresholding mechanism that transcends conventional techniques that depend on anomaly detection or statistical validation by incorporating a verification layer reminiscent of blockchain's participatory validation without the associated cryptographic overhead.

Abstract

This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks. We propose a simplified consensus-based verification process integrated with an adaptive thresholding mechanism. This dynamic thresholding is designed to adjust based on the evolving landscape of model updates, offering a refined layer of anomaly detection that aligns with the real-time needs of distributed learning environments. Our method necessitates a majority consensus among participating clients to validate updates, ensuring that only vetted and consensual modifications are applied to the global model. The efficacy of our approach is validated through experiments on two benchmark datasets in deep learning, CIFAR-10 and MNIST. Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience. This method transcends conventional techniques that depend on anomaly detection or statistical validation by incorporating a verification layer reminiscent of blockchain's participatory validation without the associated cryptographic overhead. The innovation of our approach rests in striking an optimal balance between heightened security measures and the inherent limitations of FL systems, such as computational efficiency and data privacy. Implementing a consensus mechanism specifically tailored for FL environments paves the way for more secure, robust, and trustworthy distributed machine learning applications, where safeguarding data integrity and model robustness is critical.

Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation

TL;DR

This paper proposes a simplified consensus-based verification process integrated with an adaptive thresholding mechanism that transcends conventional techniques that depend on anomaly detection or statistical validation by incorporating a verification layer reminiscent of blockchain's participatory validation without the associated cryptographic overhead.

Abstract

This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks. We propose a simplified consensus-based verification process integrated with an adaptive thresholding mechanism. This dynamic thresholding is designed to adjust based on the evolving landscape of model updates, offering a refined layer of anomaly detection that aligns with the real-time needs of distributed learning environments. Our method necessitates a majority consensus among participating clients to validate updates, ensuring that only vetted and consensual modifications are applied to the global model. The efficacy of our approach is validated through experiments on two benchmark datasets in deep learning, CIFAR-10 and MNIST. Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience. This method transcends conventional techniques that depend on anomaly detection or statistical validation by incorporating a verification layer reminiscent of blockchain's participatory validation without the associated cryptographic overhead. The innovation of our approach rests in striking an optimal balance between heightened security measures and the inherent limitations of FL systems, such as computational efficiency and data privacy. Implementing a consensus mechanism specifically tailored for FL environments paves the way for more secure, robust, and trustworthy distributed machine learning applications, where safeguarding data integrity and model robustness is critical.
Paper Structure (47 sections, 3 theorems, 6 equations, 2 figures, 1 table, 1 algorithm)

This paper contains 47 sections, 3 theorems, 6 equations, 2 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Federated Learning: Foundations and Advances
  4. Navigating Security Challenges in FL
  5. Blockchain: A Paradigm for Trust in FL
  6. Innovative Approaches to Label Verification
  7. Advancements in Defending Against Adversarial Attacks
  8. Empirical Validation: Benchmarking Security in FL
  9. Future Directions and Emerging Challenges
  10. Contributions
  11. Preliminaries
  12. Methodology
  13. Novel Integration of the Consensus-Based Label Verification Algorithm
  14. Operational Workflow in FL Training
  15. Computational Efficiency and Practical Viability
  16. ...and 32 more sections

Key Result

Theorem 1

Let $\{\theta^{(t)}\}_{t=1}^{\infty}$ be the sequence of model parameters obtained by applying the Consensus-Based Label Verification Algorithm in an FL setting with a convex loss function. Under appropriate learning rate schedules and assuming bounded gradients, this sequence converges to the optim

Figures (2)

  • Figure 1: Model Performance Over Training Epochs: The plot shows the increasing trend of model accuracy over epochs for both MNIST and CIFAR-10 datasets.
  • Figure 2: Effectiveness of the Adaptive Threshold mechanism in maintaining high model accuracy across training rounds for MNIST and CIFAR-10.

Theorems & Definitions (6)

  • Theorem 1: Convergence of the Algorithm
  • proof
  • Lemma 1: Robustness to Label-Flipping Attacks
  • proof
  • Proposition 1: Efficacy of Adaptive Threshold
  • proof