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

TL;DR

This paper introduces a novel security framework for federated learning that uses control-flow attestation techniques to verify model update integrity, significantly improving resilience against adversarial attacks while maintaining efficiency.

Contribution

It applies control-flow attestation principles to federated learning, providing a new method for ensuring model update integrity and security against adversarial threats.

Findings

Achieved 100% success in integrity verification and authentication.

Demonstrated resilience against adversarial attacks on benchmark datasets.

Maintained computational efficiency and model performance.

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.