Threat Models
- Insider attack Vs Outsider attack: An insider attack refers to an attack made by an insider ie. the server or any of its clients, whereas an outsider attack is launched by an outsider like for example a malicious intent hacker eavesdropping over the communication channel between the server and the clients. Insider attacks are generally more damaging and adverse than outsider attacks because they have more control over the FL architecture. Some common types of insider attacks are as follows:
- Single attack: a single malicious FL client causes the model to miss classify with high probability.
- Byzantine attack: this attack is similar to that of the single attack but here the client behaves in an arbitrary fashion making it difficult to find out if the model that is sent is genuine.
- Sybil attack: here the attacker simulates multiple counterfeit FL clients and supplies corrupted parameters and mounts more powerful attacks.
- Semi-honest attack Vs Malicious attack: In a semi-honest setting, the attacker is called semi-honest because he follows the FL protocol but tries to access the restricted states (such as the model parameters) of an honest client and they also stay passive but not contributing to the architecture. Whereas in case of a malicious attack the attacker arbitrarily deviates from the FL protocol and tries to access, modify, manipulate the honest client’s local training data.
- Training Phase Vs Inference Phase: Attacks in the training phase tend to influence and corrupt the FL model, they try to poison the data and compromise the integrity of the training dataset and they also try to poison the model to disrupt the learning process. In case of inference phase attacks, they do not corrupt the model or data, instead, they cause the model to produce wrong outputs and collect the model characteristics thereby compromising privacy.
Threats and vulnerabilities in Federated Learning
Prerequisites – Collaborative Learning – Federated Learning, Google Cloud Platform – Understanding Federated Learning on Cloud
In this article, we will learn review what is federated learning and its advantages over conventional machine learning algorithms. In the later part let’s try to understand the threats and vulnerabilities in federated learning architecture in simple terms.