“Decentralized AI: How Federated Learning is changing the security game”
What is Federated Learning (FL)?
Federated Learning (FL) is a machine learning paradigm where many devices or subjects learn a task together without sharing their data. FL does not rely on sending data to a central server; rather, models are updated locally, and only parameters are sent to the aggregator. This way, data is kept private since it is stored on the device, and only parameters are shared in order to create a general model.
FL was created for privacy concerns especially healthcare and mobile and edge computing etc. Further, FL is used in smart cities, IoT security, and autonomous vehicle networks, where privacy, low latency, and distributed learning are crucial.
Source: AI generated Image
Why Should a Security Engineer / Pen tester Care?
Unlike traditional machine learning models, where security concerns are mainly directed at centralized data storage and transmission, threats regarding distributed model updates, adversarial data poisoning, and model inversion attacks are specific to FL.
To this end, understanding both the attack surface and the defensive ecosystem necessary to secure the ecosystem for the integrity, confidentiality, and availability(CIA) of the federated models is crucial.
Pen testing Federated Learning Systems
- Testing for Gradient Leakage
A major security concern in FL is gradient leakage, where adversaries can learn about the private training data from the gradients that are shared during the training process. As FL operates on the basis of periodic model updates transmitted to a central server, an adversary who gets the gradients can reconstruct the data. Pen testers can assess gradient leakage in FL systems by examining the level of granularity of updates and the strength of privacy-preserving mechanisms such as differential privacy and secure aggregation, explained few of them below.
2. Data Poisoning Attack Simulation
In FL, data poisoning attacks are performed by a malicious participant who intentionally manipulates the participant’s local training data to reduce the model performance or incorporate biases. This has severe implications in applications such as healthcare and finance, where data integrity is essential. Pen testers can inject subtle modifications into training datasets to simulate poisoning attacks and then assess their impact on model accuracy and predictions.
3. Intercepting & Modifying Model Updates
Because FL works based on model update transmission over a network, the attackers can launch their attacks on these updates before they reach the aggregator. This opens the door for adversaries to open backdoors, change model parameters, or even reduce the efficiency of the model. Man-in-the-middle attackers can target FL communications to look for weaknesses in transport security and integrity verification.
4. Backdoor Injection
Backdoor attacks are intended to hide triggers within the models of FL in order to cause the models to generate unwanted outputs in certain circumstances. For instance, an adversary may manipulate a federated image recognition model to misidentify particular objects unless a specific pattern exists. In this case, pen-testers can determine the strength of the FL systems against such attacks by looking at model updates and looking for unwanted bias or trigger-based misclassifications.
Security Best Practices for Federated Learning:
In order to address security risks in FL, organizations should follow best practices. Some of them are secure aggregation that uses homomorphic encryption or differential privacy to avoid gradient leakage. These methods ensure that the gradients are shared in a way that does not enable an adversary to reconstruct the gradients of the sensitive data.
Another important security countermeasure is participant identification and data integrity check. Organizations should use cryptographic digital signatures and attestation to confirm that only permitted entities are involved in the FL training. This minimizes the risk of adversaries submitting rogue updates or altering the model parameters.
Furthermore, organizations should use anomaly detection and adversarial testing tools to detect any unusual activities in the FL training process. Early detection can help prevent malicious participants or model behaviors that can cause significant damage.
Last but not least, organizations should perform security audits and penetration tests on their FL systems on a regular basis. Through proactive vulnerability management and robust security management measures, companies can enhance the robustness of their federated models against increasing threats (Esteves, 2022).
Cybersecurity professionals must be ready for the growing threats that come with the rising popularity of FL in various fields as FL keeps on growing. Employing a security-first approach, alongside ongoing monitoring and testing, will be vital to advancing the adoption of safe federated learning in critical industries.
References:
Ning, W., Zhu, Y., Song, C., Li, H., Zhu, L., Xie, J., Chen, T., Xu, T., Xu, X., & Gao, J. (2024). Blockchain-Based Federated Learning: A Survey and New Perspectives. Applied Sciences, 14(20), 9459. https://doi.org/10.3390/app14209459
Esteves, L. G. (2022). Federated Learning for iot Edge Computing: An Experimental Study (Order №31175273). Available from ProQuest Dissertations & Theses Global. (3098763302). https://www.proquest.com/dissertations-theses/federated-learning-iot-edge-computing/docview/3098763302/se-2
