Turning Federated Learning Systems Into Covert Channels
Gabriele Costa, Fabio Pinelli, Simone Soderi, Gabriele Tolomei
Abstract
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. This paper proves that FL systems can be turned into covert channels to implement a stealth communication infrastructure. The main intuition is that, during federated training, a malicious sender can poison the global model by submitting purposely crafted examples. Although the effect of the model poisoning is negligible to other participants and does not alter the overall model performance, it can be observed by a malicious receiver and used to transmit a sequence of bits. We mounted our attack on an FL system to verify its feasibility. Experimental evidence shows that this covert channel is reliable, efficient, and extremely hard to counter. These results highlight that our new attacker model threatens FL infrastructures.