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Turning Federated Learning Systems Into Covert Channels
Feb. 26, 2024, 5:11 a.m. | Gabriele Costa, Fabio Pinelli, Simone Soderi, Gabriele Tolomei
cs.CR updates on arXiv.org arxiv.org
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. In this paper, we put forward a novel attacker model aiming at turning FL systems into covert channels to implement a stealth communication …
arxiv beyond clients cloud collection covert cs.cr cs.lg data edge federated federated learning global goes large local machine machine learning model training private systems train training training data
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