Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Securing Federated Learning with Control-Flow Attestation: A Novel Framework for Enhanced Integrity and Resilience against Adversarial Attacks

Zahir Alsulaimawi

TL;DR

This study proposes an innovative security framework inspired by Control-Flow Attestation mechanisms, traditionally used in cybersecurity, to ensure software execution integrity, by integrating digital signatures and cryptographic hashing within the FL framework.

Abstract

The advent of Federated Learning (FL) as a distributed machine learning paradigm has introduced new cybersecurity challenges, notably adversarial attacks that threaten model integrity and participant privacy. This study proposes an innovative security framework inspired by Control-Flow Attestation (CFA) mechanisms, traditionally used in cybersecurity, to ensure software execution integrity. By integrating digital signatures and cryptographic hashing within the FL framework, we authenticate and verify the integrity of model updates across the network, effectively mitigating risks associated with model poisoning and adversarial interference. Our approach, novel in its application of CFA principles to FL, ensures contributions from participating nodes are authentic and untampered, thereby enhancing system resilience without compromising computational efficiency or model performance. Empirical evaluations on benchmark datasets, MNIST and CIFAR-10, demonstrate our framework's effectiveness, achieving a 100\% success rate in integrity verification and authentication and notable resilience against adversarial attacks. These results validate the proposed security enhancements and open avenues for more secure, reliable, and privacy-conscious distributed machine learning solutions. Our work bridges a critical gap between cybersecurity and distributed machine learning, offering a foundation for future advancements in secure FL.

Securing Federated Learning with Control-Flow Attestation: A Novel Framework for Enhanced Integrity and Resilience against Adversarial Attacks

TL;DR

This study proposes an innovative security framework inspired by Control-Flow Attestation mechanisms, traditionally used in cybersecurity, to ensure software execution integrity, by integrating digital signatures and cryptographic hashing within the FL framework.

Abstract

The advent of Federated Learning (FL) as a distributed machine learning paradigm has introduced new cybersecurity challenges, notably adversarial attacks that threaten model integrity and participant privacy. This study proposes an innovative security framework inspired by Control-Flow Attestation (CFA) mechanisms, traditionally used in cybersecurity, to ensure software execution integrity. By integrating digital signatures and cryptographic hashing within the FL framework, we authenticate and verify the integrity of model updates across the network, effectively mitigating risks associated with model poisoning and adversarial interference. Our approach, novel in its application of CFA principles to FL, ensures contributions from participating nodes are authentic and untampered, thereby enhancing system resilience without compromising computational efficiency or model performance. Empirical evaluations on benchmark datasets, MNIST and CIFAR-10, demonstrate our framework's effectiveness, achieving a 100\% success rate in integrity verification and authentication and notable resilience against adversarial attacks. These results validate the proposed security enhancements and open avenues for more secure, reliable, and privacy-conscious distributed machine learning solutions. Our work bridges a critical gap between cybersecurity and distributed machine learning, offering a foundation for future advancements in secure FL.
Paper Structure (28 sections, 3 theorems, 6 equations, 3 figures, 4 tables, 3 algorithms)

This paper contains 28 sections, 3 theorems, 6 equations, 3 figures, 4 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Comparison with Existing Security Solutions
  4. Advancements in Cryptographic Solutions for FL
  5. Real-World Applications and Case Studies
  6. Challenges and Limitations of Applying CFA in FL
  7. Emerging Threats and Future Directions in FL Security
  8. Survey and Review Papers on FL Security
  9. Proposed Method
  10. Local Model Update Generation
  11. Secure Model Aggregation
  12. Control-Flow Attestation
  13. Algorithmic Representation
  14. Algorithm 1: Secure Local Model Update Generation
  15. Algorithm 2: Secure Model Aggregation and Verification
  16. ...and 13 more sections

Key Result

Theorem 1

(Security Guarantees of the CFA-Inspired FL System) Let $\mathcal{F}$ denote an FL system incorporating CFA-inspired security mechanisms, including digital signatures for authentication, cryptographic hashing for data integrity, and CFA for execution path verification. Against any polynomial-time ad

Figures (3)

  • Figure 1: Impact of Security Measures on Model Accuracy Under Different Attacks.
  • Figure 2: MNIST Dataset Accuracy Retention with and without Security Measures under Adversarial Attacks.
  • Figure 3: CIFAR-10 Dataset Accuracy Retention with and without Security Measures under Adversarial Attacks.

Theorems & Definitions (6)

  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • Theorem 3: Scalability of CFA-Inspired FL System
  • proof