Implantable Cardioverter Defibrillator (ICD) is an ultra-low-power device which monitors heart rate and delivers in-time defibrillation on detected ventricular arrhythmias (VAs). The parameters of VAs detection mechanism on each recipient’s ICD are supposed to be fine-tuned to obtain accurate detection due to the individual’s unique rhythm features. However, the process extremely relies on clinical expertise and thus must be conducted manually and routinely by cardiologists diagnosing massive amount of rhythm data. In this paper, we introduce a novel self-supervised on-device personalization of convolutional neural network (CNNs) for VAs detection. We first propose a computing framework consisting of an edge device and an ICD to enable efficient on-device CNNs personalization and real-time inference respectively. Then, we propose a generative model that learns to synthesize patient-specific intracardiac EGMs signals, which can then be used as personalized training data to improve patient-specific VAs detection performance on ICDs. Evaluations on three detection models show that the self-supervised on-device personalization significantly improve VAs detection performance under a patient-specific setting.