  Machine Learning (ML)-powered apps are used in pervasive devices such as
phones, tablets, smartwatches and IoT devices. Recent advances in
collaborative, distributed ML such as Federated Learning (FL) attempt to solve
privacy concerns of users and data owners, and thus used by tech industry
leaders such as Google, Facebook and Apple. However, FL systems and models are
still vulnerable to adversarial membership and attribute inferences and model
poisoning attacks, especially in FL-as-a-Service ecosystems recently proposed,
which can enable attackers to access multiple ML-powered apps. In this work, we
focus on the recently proposed Sponge attack: It is designed to soak up energy
consumed while executing inference (not training) of ML model, without
hampering the classifier's performance. Recent work has shown sponge attacks on
ASCI-enabled GPUs can potentially escalate the power consumption and inference
time. For the first time, in this work, we investigate this attack in the
mobile setting and measure the effect it can have on ML models running inside
apps on mobile devices.
