Effective Model Poisoning Attacks to Federated Learning via Consistent Malicious Updates

Core Concepts

PoisonedFL, a novel model poisoning attack, leverages consistent malicious model updates across training rounds to substantially degrade the performance of the final global model, without requiring any knowledge about genuine clients' local training data or models.

Abstract

The paper proposes PoisonedFL, a novel model poisoning attack to Federated Learning (FL) systems. The key insights are:

Existing model poisoning attacks suffer from suboptimal effectiveness due to the self-cancellation of their malicious model updates across training rounds. This is because they only leverage consistency within individual training rounds, but neglect consistency across multiple rounds.

PoisonedFL addresses this limitation by enforcing multi-round consistency in the malicious model updates. Specifically, it crafts the malicious model updates on fake clients such that the total aggregated model updates across rounds have a large magnitude along a random update direction. This substantially degrades the final global model's performance.

PoisonedFL does not require any knowledge about genuine clients' local training data or models, making it a practical threat. It dynamically adjusts the magnitude of the malicious model updates to avoid being filtered out by the unknown defense deployed by the server.

Extensive experiments show that PoisonedFL breaks eight state-of-the-art FL defenses and outperforms seven existing model poisoning attacks, even when the latter have access to genuine clients' information.

The paper also explores new defenses tailored to PoisonedFL, but demonstrates that PoisonedFL can still be adapted to counter them with minor impact on its effectiveness.

Stats

The final global model has a testing error rate of up to 95% under PoisonedFL, compared to 2-35% without any attack.
PoisonedFL achieves higher testing error rates than existing attacks in most cases, even when the latter have access to genuine clients' information.
PoisonedFL remains effective across different FL settings, including the fraction of malicious clients, the degree of non-IID data, the number of local training epochs, and the fraction of clients selected in each round.

Quotes

"Our key observation is that a model, when updated substantially in a random direction, exhibits significantly degraded accuracy."
"The attacker crafts the malicious model updates such that the aggregated model update across multiple training rounds has a large magnitude along a random update direction, leading to a large testing error rate of the final global model."
"PoisonedFL does not require any knowledge about genuine clients' local training data or models, making it a practical threat."

Key Insights Distilled From

PoisonedFL: Model Poisoning Attacks to Federated Learning via Multi-Round Consistency

by Yueqi Xie,Mi... at arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15611.pdf

Deeper Inquiries

How can the FL system be made more robust against PoisonedFL-style attacks that leverage consistent malicious updates across rounds?

To make the FL system more robust against PoisonedFL-style attacks that leverage consistent malicious updates across rounds, several strategies can be implemented:

Dynamic Defense Mechanisms: Implement dynamic defense mechanisms that can adapt to the evolving nature of the attacks. This can include continuously monitoring the model updates and adjusting the defense strategies based on the patterns observed.

Enhanced Detection Techniques: Develop more advanced detection techniques to identify fake clients or malicious model updates. This can involve using anomaly detection algorithms, machine learning models, or behavioral analysis to detect unusual patterns in the model updates.

Randomization Techniques: Introduce randomization techniques in the aggregation process to make it harder for attackers to predict the impact of their malicious updates. This can include randomizing the selection of clients for participation in each round or adding noise to the aggregation process.

Collaborative Defense: Implement collaborative defense strategies where multiple parties work together to detect and mitigate attacks. This can involve sharing information about potential threats and coordinating responses to mitigate the impact of attacks.

Regular Security Audits: Conduct regular security audits of the FL system to identify vulnerabilities and weaknesses that could be exploited by attackers. This can help in proactively addressing security issues before they are exploited.

By implementing these strategies, the FL system can enhance its resilience against PoisonedFL-style attacks and mitigate the impact of consistent malicious updates across rounds.

What are the potential implications of PoisonedFL-style attacks on the real-world deployment and adoption of FL systems?

The implications of PoisonedFL-style attacks on the real-world deployment and adoption of FL systems are significant and can have far-reaching consequences:

Trust and Credibility: PoisonedFL-style attacks can erode trust and credibility in FL systems, especially in sensitive domains like healthcare and finance. If attackers can manipulate the global model to make incorrect predictions, it can lead to serious consequences and undermine the reliability of the system.

Data Privacy Concerns: FL systems rely on the collaboration of multiple clients without sharing raw data. However, if malicious actors can inject fake clients and manipulate the model, it raises concerns about data privacy and security. Users may be hesitant to participate in FL systems if their data is at risk of being compromised.

Regulatory Compliance: With the increasing focus on data privacy regulations like GDPR and HIPAA, FL systems must ensure compliance with regulatory requirements. PoisonedFL-style attacks can violate data protection laws and regulations, leading to legal implications and penalties.

Financial Loss: A successful PoisonedFL attack can result in financial losses for organizations deploying FL systems. If the manipulated model leads to incorrect decisions or predictions, it can impact business operations, customer trust, and revenue.

Reputation Damage: In the event of a successful PoisonedFL attack, the reputation of the organization implementing the FL system can be severely damaged. This can have long-term consequences on customer trust, partnerships, and market competitiveness.

Overall, PoisonedFL-style attacks pose a significant threat to the real-world deployment and adoption of FL systems, highlighting the importance of robust security measures and defense mechanisms to safeguard against such attacks.

Can the insights from PoisonedFL be applied to other distributed learning paradigms beyond FL to uncover their vulnerabilities?

Yes, the insights from PoisonedFL can be applied to other distributed learning paradigms beyond FL to uncover their vulnerabilities and enhance their security. Some ways in which these insights can be applied include:

Model Poisoning Attacks: The concept of leveraging consistent malicious updates across rounds to undermine the learning process can be applied to other distributed learning paradigms. By studying how attackers can manipulate the learning process over multiple rounds, vulnerabilities in different distributed learning systems can be identified and addressed.

Defense Mechanisms: The strategies used in PoisonedFL to defend against model poisoning attacks, such as dynamic defense mechanisms and enhanced detection techniques, can be adapted to other distributed learning paradigms. By implementing robust defense mechanisms, vulnerabilities in the learning process can be mitigated.

Security Audits: Conducting regular security audits to identify vulnerabilities and weaknesses in distributed learning systems can help uncover potential threats and enhance the overall security posture. By applying the insights from PoisonedFL, organizations can proactively address security issues and strengthen their defenses.

Collaborative Security: Collaborative defense strategies, where multiple parties work together to detect and mitigate attacks, can be extended to other distributed learning paradigms. By sharing information and coordinating responses, organizations can enhance their resilience against security threats.

By applying the insights from PoisonedFL to other distributed learning paradigms, organizations can uncover vulnerabilities, strengthen their security measures, and ensure the integrity and reliability of their learning systems.