  Split Learning (SL) and Federated Learning (FL) are two prominent distributed
collaborative learning techniques that maintain data privacy by allowing
clients to never share their private data with other clients and servers, and
fined extensive IoT applications in smart healthcare, smart cities, and smart
industry. Prior work has extensively explored the security vulnerabilities of
FL in the form of poisoning attacks. To mitigate the effect of these attacks,
several defenses have also been proposed. Recently, a hybrid of both learning
techniques has emerged (commonly known as SplitFed) that capitalizes on their
advantages (fast training) and eliminates their intrinsic disadvantages
(centralized model updates). In this paper, we perform the first ever empirical
analysis of SplitFed's robustness to strong model poisoning attacks. We observe
that the model updates in SplitFed have significantly smaller dimensionality as
compared to FL that is known to have the curse of dimensionality. We show that
large models that have higher dimensionality are more susceptible to privacy
and security attacks, whereas the clients in SplitFed do not have the complete
model and have lower dimensionality, making them more robust to existing model
poisoning attacks. Our results show that the accuracy reduction due to the
model poisoning attack is 5x lower for SplitFed compared to FL.

Machine Learning;Artificial Intelligence;Cryptography and Security