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Federated Learning with Anomaly Detection via Gradient and Reconstruction Analysis

Zahir Alsulaimawi

TL;DR

This work introduces a novel framework that synergizes gradient-based analysis with autoencoder-driven data reconstruction to detect and mitigate poisoned data with unprecedented precision, significantly enhancing FL model security.

Abstract

In the evolving landscape of Federated Learning (FL), the challenge of ensuring data integrity against poisoning attacks is paramount, particularly for applications demanding stringent privacy preservation. Traditional anomaly detection strategies often struggle to adapt to the distributed nature of FL, leaving a gap our research aims to bridge. We introduce a novel framework that synergizes gradient-based analysis with autoencoder-driven data reconstruction to detect and mitigate poisoned data with unprecedented precision. Our approach uniquely combines detecting anomalous gradient patterns with identifying reconstruction errors, significantly enhancing FL model security. Validated through extensive experiments on MNIST and CIFAR-10 datasets, our method outperforms existing solutions by 15\% in anomaly detection accuracy while maintaining a minimal false positive rate. This robust performance, consistent across varied data types and network sizes, underscores our framework's potential in securing FL deployments in critical domains such as healthcare and finance. By setting new benchmarks for anomaly detection within FL, our work paves the way for future advancements in distributed learning security.

Federated Learning with Anomaly Detection via Gradient and Reconstruction Analysis

TL;DR

This work introduces a novel framework that synergizes gradient-based analysis with autoencoder-driven data reconstruction to detect and mitigate poisoned data with unprecedented precision, significantly enhancing FL model security.

Abstract

In the evolving landscape of Federated Learning (FL), the challenge of ensuring data integrity against poisoning attacks is paramount, particularly for applications demanding stringent privacy preservation. Traditional anomaly detection strategies often struggle to adapt to the distributed nature of FL, leaving a gap our research aims to bridge. We introduce a novel framework that synergizes gradient-based analysis with autoencoder-driven data reconstruction to detect and mitigate poisoned data with unprecedented precision. Our approach uniquely combines detecting anomalous gradient patterns with identifying reconstruction errors, significantly enhancing FL model security. Validated through extensive experiments on MNIST and CIFAR-10 datasets, our method outperforms existing solutions by 15\% in anomaly detection accuracy while maintaining a minimal false positive rate. This robust performance, consistent across varied data types and network sizes, underscores our framework's potential in securing FL deployments in critical domains such as healthcare and finance. By setting new benchmarks for anomaly detection within FL, our work paves the way for future advancements in distributed learning security.
Paper Structure (32 sections, 3 theorems, 14 equations, 11 figures, 1 algorithm)

This paper contains 32 sections, 3 theorems, 14 equations, 11 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Broader Implications
  3. Related Work
  4. Proposed Method
  5. Gradient Analysis for Anomaly Detection
  6. Data Reconstruction with Autoencoders
  7. Integration in Federated Learning
  8. Theoretical Analysis
  9. Mathematical Formulation
  10. Model Training and Update Aggregation
  11. Anomaly Detection via Gradient Analysis
  12. Data Reconstruction Analysis
  13. Objective Function
  14. Theoretical Guarantees
  15. Complexity Analysis
  16. ...and 17 more sections

Key Result

Theorem 1

Let $\{w^t\}_{t=1}^{\infty}$ be the sequence of global model parameters obtained from the federated learning algorithm with gradient-based anomaly detection over $T$ rounds. Assuming the global loss function $F(w)$ is convex, $L$-smooth, and the learning rate $\eta_t$ satisfies the conditions $\eta_

Figures (11)

  • Figure 1: ROC curves comparison
  • Figure 2: Model Performance and Anomaly Detection vs. Sensitivity Factor for MNIST (Smoothed)
  • Figure 3: Model Performance and Anomaly Detection vs. Sensitivity Factor for CIFAR-10 (Smoothed)
  • Figure 4: Anomly Detection Metrics vs. Sensitivity Factor for MNIST
  • Figure 5: Model Performance Metrics vs Sensitivity Factor for MNIST
  • ...and 6 more figures

Theorems & Definitions (6)

  • Theorem 1: Convergence of Federated Learning with Anomaly Detection
  • proof
  • Theorem 2: Sensitivity to Anomaly Detection Parameters
  • proof
  • Theorem 3: Convergence and Robustness with Anomaly Detection
  • proof