Can You Really Backdoor Federated Learning? (1911.07963v2)

Abstract: The decentralized nature of federated learning makes detecting and defending against adversarial attacks a challenging task. This paper focuses on backdoor attacks in the federated learning setting, where the goal of the adversary is to reduce the performance of the model on targeted tasks while maintaining good performance on the main task. Unlike existing works, we allow non-malicious clients to have correctly labeled samples from the targeted tasks. We conduct a comprehensive study of backdoor attacks and defenses for the EMNIST dataset, a real-life, user-partitioned, and non-iid dataset. We observe that in the absence of defenses, the performance of the attack largely depends on the fraction of adversaries present and the "complexity'' of the targeted task. Moreover, we show that norm clipping and "weak'' differential privacy mitigate the attacks without hurting the overall performance. We have implemented the attacks and defenses in TensorFlow Federated (TFF), a TensorFlow framework for federated learning. In open-sourcing our code, our goal is to encourage researchers to contribute new attacks and defenses and evaluate them on standard federated datasets.

• The paper reveals that targeted backdoor attacks can impair sub-task performance in federated learning even with a small fraction of adversarial clients.
• The paper identifies norm clipping and weak differential privacy as effective countermeasures against model update poisoning attacks.
• The paper outlines future research directions focused on refining defense thresholds to bolster model resilience against evolving adversarial strategies.

An Insightful Overview of "Can You Really Backdoor Federated Learning?"

This paper, authored by Ziteng Sun and collaborators, examines the vulnerability of federated learning systems to backdoor attacks. The paper specifically addresses the challenge posed by adversaries aiming to impair model performance on targeted tasks without significantly affecting overall main task accuracy. The central focus of this research lies in analyzing the effectiveness of such attacks and the potential defenses within a federated learning context.

Overview and Key Contributions

Federated learning, as a decentralized approach, presents unique difficulties in identifying and mitigating adversarial threats. This research zeroes in on backdoor attacks—a form of targeted adversarial attack where specific sub-tasks are compromised. The exploration is comprehensive, rooted in experiments conducted on the EMNIST dataset to simulate realistic federated settings.

Unlike previous studies, this work introduces a scenario where benign clients possess accurately labeled samples from the targeted sub-tasks, providing a more stringent testbed for attack strategies and defense mechanisms. The paper systematically evaluates various attack scenarios, primarily focusing on model update poisoning attacks implemented through TensorFlow Federated (TFF).

Experimental Findings

The experimental outcomes reveal critical insights into the mechanics of backdoor attacks:

• Adversary Fraction and Task Complexity: The effectiveness of backdoor attacks correlates strongly with the fraction of adversarial clients involved and the complexity of targeted tasks. The more adversaries and simpler the backdoor tasks, the higher the attack success rate.
• Defense Mechanisms: Notably, norm clipping and differential privacy are illustrated as effective defenses. Norm clipping curtails the attack's impact by constraining update magnitudes, while a "weak" application of differential privacy—adding small noise—further mitigates attacks without degrading primary task performance.

Implications and Future Research

The implications of this research are twofold. Practically, it suggests concrete defense strategies that can be incorporated into federated learning systems to enhance robustness against targeted adversarial threats. Theoretically, it opens avenues for future work to explore more sophisticated attack strategies and counter-defensive innovations.

Potential future directions could focus on optimizing norm-bound thresholds and noise levels in privacy-preserving techniques. Research could also explore attack adaptability over iterations or enhanced coordination among adversaries.

Conclusion

The paper provides a methodologically sound exploration of backdoor vulnerabilities within federated learning systems. By addressing the adversarial models with realistic constraints and evaluating robust defense mechanisms, it contributes significantly to the ongoing discourse on securing distributed learning paradigms. This work lays a foundation for further explorations into strengthening model resilience against increasingly sophisticated threats in federated environments.