Video Frame Interpolation (VFI), which aims at generating high-frame-rate videos from low-frame-rate inputs, is a highly challenging task. The emergence of bio-inspired sensors known as event cameras, which boast microsecond-level temporal resolution, has ushered in a transformative era for VFI. Nonetheless, the application of event-based VFI techniques in domains with distinct environments from the training data can be problematic. This is mainly because event camera data distribution can undergo substantial variations based on camera settings and scene conditions, presenting challenges for effective adaptation. In this paper, we propose a test-time adaptation method for event-based VFI to address the gap between the source and target domains. Our approach enables sequential learning in an online manner on the target domain, which only provides low-frame-rate videos. We present an approach that leverages confident pixels as pseudo ground-truths, enabling stable and accurate online learning from low-frame-rate videos. Furthermore, to prevent overfitting during the continuous online process where the same scene is encountered repeatedly, we propose a method of blending historical samples with current scenes. Extensive experiments validate the effectiveness of our method, both in cross-domain and continuous domain shifting setups. We will make our code and dataset publicly available.