Turning Federated Learning Systems Into Covert Channels

Understand

Federated learning (FL) goes beyond traditional, centralized machine learning by distributing model training among a large collection of edge clients.

• These clients cooperatively train a global, e.g., cloud-hosted, model without disclosing their local, private training data.
• The global model is then shared among all the participants which use it for local predictions.
• In this paper, we put forward a novel attacker model aiming at turning FL systems into covert channels to implement a stealth communication infrastructure.